{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NEST implementation of the `aeif` models\n",
    "\n",
    "#### Hans Ekkehard Plesser and Tanguy Fardet, 2016-09-09\n",
    "\n",
    "This notebook provides a reference solution for the _Adaptive Exponential Integrate and Fire_\n",
    "(AEIF) neuronal model and compares it with several numerical implementation using simpler solvers.\n",
    "In particular this justifies the change of implementation in September 2016 to make the simulation\n",
    "closer to the reference solution.\n",
    "\n",
    "## Position of the problem\n",
    "\n",
    "### Basics\n",
    "The equations governing the evolution of the AEIF model are\n",
    "\n",
    "$$\\left\\lbrace\\begin{array}{rcl}\n",
    "    C_m\\dot{V} &=& -g_L(V-E_L) + g_L \\Delta_T e^{\\frac{V-V_T}{\\Delta_T}} + I_e + I_s(t) -w\\\\\n",
    "    \\tau_s\\dot{w} &=& a(V-E_L) - w\n",
    "\\end{array}\\right.$$\n",
    "\n",
    "when $V < V_{peak}$ (threshold/spike detection).\n",
    "Once a spike occurs, we apply the reset conditions:\n",
    "\n",
    "$$V = V_r \\quad \\text{and} \\quad w = w + b$$\n",
    "\n",
    "### Divergence\n",
    "In the AEIF model, the spike is generated by the exponential divergence. In practice, this means that just before threshold crossing (threshpassing), the argument of the exponential can become very large.\n",
    "\n",
    "This can lead to numerical overflow or numerical instabilities in the solver, all the more if $V_{peak}$ is large, or if $\\Delta_T$ is small.\n",
    "\n",
    "## Tested solutions\n",
    "\n",
    "### Old implementation (before September 2016)\n",
    "The orginal solution that was adopted was to bind the exponential argument to be smaller that 10 (ad hoc value to be close to the original implementation in BRIAN).\n",
    "As will be shown in the notebook, this solution does not converge to the reference LSODAR solution.\n",
    "\n",
    "### New implementation\n",
    "The new implementation does not bind the argument of the exponential, but the potential itself, since according to the theoretical model, $V$ should never get larger than $V_{peak}$.\n",
    "We will show that this solution is not only closer to the reference solution in general, but also converges towards it as the timestep gets smaller.\n",
    "\n",
    "## Reference solution\n",
    "\n",
    "The reference solution is implemented using the LSODAR solver which is described and compared in the following references:\n",
    "\n",
    "* http://www.radford.edu/~thompson/RP/eventlocation.pdf (papers citing this one)\n",
    "* http://www.sciencedirect.com/science/article/pii/S0377042712000684\n",
    "* http://www.radford.edu/~thompson/RP/rootfinding.pdf\n",
    "* https://computation.llnl.gov/casc/nsde/pubs/u88007.pdf\n",
    "* http://www.cs.ucsb.edu/~cse/Files/SCE000136.pdf\n",
    "* http://www.sciencedirect.com/science/article/pii/0377042789903348\n",
    "* http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.455.2976&rep=rep1&type=pdf\n",
    "* https://theses.lib.vt.edu/theses/available/etd-12092002-105032/unrestricted/etd.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Technical details and requirements\n",
    "\n",
    "### Implementation of the functions\n",
    "\n",
    "* The old and new implementations are reproduced using Scipy and are called by the ``scipy_aeif`` function\n",
    "* The NEST implementations are not shown here, but keep in mind that for a given time resolution, they are closer to the reference result than the scipy implementation since the GSL implementation uses a RK45 adaptive solver.\n",
    "* The reference solution using LSODAR, called ``reference_aeif``, is implemented through the [assimulo](http://www.jmodelica.org/assimulo) package.\n",
    "\n",
    "### Requirements\n",
    "\n",
    "To run this notebook, you need:\n",
    "\n",
    "* [numpy](http://www.numpy.org/) and [scipy](http://www.scipy.org/)\n",
    "* [assimulo](http://www.jmodelica.org/assimulo)\n",
    "* [matplotlib](http://matplotlib.org/)\n",
    "\n",
    "[//]: # (And [NEST](https://www.nest-simulator.org/download/) to run the appendix.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.integrate import odeint\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (15, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scipy functions mimicking the NEST code\n",
    "### Right hand side functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rhs_aeif_new(y, _, p):\n",
    "    '''\n",
    "    New implementation bounding V < V_peak\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    y : list\n",
    "        Vector containing the state variables [V, w]\n",
    "    _ : unused var\n",
    "    p : Params instance\n",
    "        Object containing the neuronal parameters.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    dv : double\n",
    "        Derivative of V\n",
    "    dw : double\n",
    "        Derivative of w\n",
    "    '''\n",
    "    v = min(y[0], p.Vpeak)\n",
    "    w = y[1]\n",
    "    Ispike = 0.\n",
    "    \n",
    "    if p.DeltaT != 0.:\n",
    "        Ispike = p.gL * p.DeltaT * np.exp((v-p.vT)/p.DeltaT)\n",
    "        \n",
    "    dv = (-p.gL*(v-p.EL) + Ispike - w + p.Ie)/p.Cm\n",
    "    dw = (p.a * (v-p.EL) - w) / p.tau_w\n",
    "    \n",
    "    return dv, dw\n",
    "\n",
    "\n",
    "def rhs_aeif_old(y, _, p):\n",
    "    '''\n",
    "    Old implementation bounding the argument of the\n",
    "    exponential function (e_arg < 10.).\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    y : list\n",
    "        Vector containing the state variables [V, w]\n",
    "    _ : unused var\n",
    "    p : Params instance\n",
    "        Object containing the neuronal parameters.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    dv : double\n",
    "        Derivative of V\n",
    "    dw : double\n",
    "        Derivative of w\n",
    "    '''\n",
    "    v = y[0]\n",
    "    w = y[1]\n",
    "    Ispike = 0.\n",
    "    \n",
    "    if p.DeltaT != 0.:\n",
    "        e_arg = min((v-p.vT)/p.DeltaT, 10.)\n",
    "        Ispike = p.gL * p.DeltaT * np.exp(e_arg)\n",
    "        \n",
    "    dv = (-p.gL*(v-p.EL) + Ispike - w + p.Ie)/p.Cm\n",
    "    dw = (p.a * (v-p.EL) - w) / p.tau_w\n",
    "    \n",
    "    return dv, dw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Complete model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def scipy_aeif(p, f, simtime, dt):\n",
    "    '''\n",
    "    Complete aeif model using scipy `odeint` solver.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    p : Params instance\n",
    "        Object containing the neuronal parameters.\n",
    "    f : function\n",
    "        Right-hand side function (either `rhs_aeif_old`\n",
    "        or `rhs_aeif_new`)\n",
    "    simtime : double\n",
    "        Duration of the simulation (will run between\n",
    "        0 and tmax)\n",
    "    dt : double\n",
    "        Time increment.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    t : list\n",
    "        Times at which the neuronal state was evaluated.\n",
    "    y : list\n",
    "        State values associated to the times in `t`\n",
    "    s : list\n",
    "        Spike times.\n",
    "    vs : list\n",
    "        Values of `V` just before the spike.\n",
    "    ws : list\n",
    "        Values of `w` just before the spike\n",
    "    fos : list\n",
    "        List of dictionaries containing additional output\n",
    "        information from `odeint`\n",
    "    '''\n",
    "    t = np.arange(0, simtime, dt)   # time axis\n",
    "    n = len(t)                 \n",
    "    y = np.zeros((n, 2))         # V, w\n",
    "    y[0, 0] = p.EL               # Initial: (V_0, w_0) = (E_L, 5.)\n",
    "    y[0, 1] = 5.                 # Initial: (V_0, w_0) = (E_L, 5.)\n",
    "    s = []      # spike times                 \n",
    "    vs = []     # membrane potential at spike before reset\n",
    "    ws = []     # w at spike before step\n",
    "    fos = []    # full output dict from odeint()\n",
    "    \n",
    "    # imitate NEST: update time-step by time-step\n",
    "    for k in range(1, n):\n",
    "        \n",
    "        # solve ODE from t_k-1 to t_k\n",
    "        d, fo = odeint(f, y[k-1, :], t[k-1:k+1], (p, ), full_output=True)\n",
    "        y[k, :] = d[1, :]\n",
    "        fos.append(fo)\n",
    "        \n",
    "        # check for threshold crossing\n",
    "        if y[k, 0] >= p.Vpeak:\n",
    "            s.append(t[k])\n",
    "            vs.append(y[k, 0])\n",
    "            ws.append(y[k, 1])\n",
    "            \n",
    "            y[k, 0] = p.Vreset  # reset\n",
    "            y[k, 1] += p.b      # step\n",
    "            \n",
    "    return t, y, s, vs, ws, fos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSODAR reference solution\n",
    "### Setting assimulo class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "libsundials_nvecserial.so.2: cannot open shared object file: No such file or directory",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-6fb744ce4318>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0massimulo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolvers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLSODAR\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0massimulo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproblem\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mExplicit_Problem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mExtended_Problem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mExplicit_Problem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/assimulo/solvers/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0meuler\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mExplicitEuler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mImplicitEuler\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mradau5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRadau5ODE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRadau5DAE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_Radau5ODE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_Radau5DAE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0msundials\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mIDA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCVode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mkinsol\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mKINSOL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mrunge_kutta\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRungeKutta34\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRungeKutta4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDopri5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: libsundials_nvecserial.so.2: cannot open shared object file: No such file or directory"
     ]
    }
   ],
   "source": [
    "from assimulo.solvers import LSODAR\n",
    "from assimulo.problem import Explicit_Problem\n",
    "\n",
    "class Extended_Problem(Explicit_Problem):\n",
    "\n",
    "    # need variables here for access\n",
    "    sw0 = [ False ]\n",
    "    ts_spikes = []\n",
    "    ws_spikes = []\n",
    "    Vs_spikes = []\n",
    "    \n",
    "    def __init__(self, p):\n",
    "        self.p = p\n",
    "        self.y0 = [self.p.EL, 5.]   # V, w\n",
    "        # reset variables\n",
    "        self.ts_spikes = []\n",
    "        self.ws_spikes = []\n",
    "        self.Vs_spikes = []\n",
    "\n",
    "    #The right-hand-side function (rhs)\n",
    "\n",
    "    def rhs(self, t, y, sw):\n",
    "        \"\"\"\n",
    "        This is the function we are trying to simulate (aeif model).\n",
    "        \"\"\"\n",
    "        V, w = y[0], y[1]\n",
    "        Ispike = 0.\n",
    "        \n",
    "        if self.p.DeltaT != 0.:\n",
    "            Ispike = self.p.gL * self.p.DeltaT * np.exp((V-self.p.vT)/self.p.DeltaT)\n",
    "        dotV = ( -self.p.gL*(V-self.p.EL) + Ispike + self.p.Ie - w ) / self.p.Cm\n",
    "        dotW = ( self.p.a*(V-self.p.EL) - w ) / self.p.tau_w\n",
    "        return np.array([dotV, dotW])\n",
    "\n",
    "    # Sets a name to our function\n",
    "    name = 'AEIF_nosyn'\n",
    "\n",
    "    # The event function\n",
    "    def state_events(self, t, y, sw):\n",
    "        \"\"\"\n",
    "        This is our function that keeps track of our events. When the sign\n",
    "        of any of the events has changed, we have an event.\n",
    "        \"\"\"\n",
    "        event_0 = -5 if y[0] >= self.p.Vpeak else 5 # spike\n",
    "        if event_0 < 0:\n",
    "            if not self.ts_spikes:\n",
    "                self.ts_spikes.append(t)\n",
    "                self.Vs_spikes.append(y[0])\n",
    "                self.ws_spikes.append(y[1])\n",
    "            elif self.ts_spikes and not np.isclose(t, self.ts_spikes[-1], 0.01):\n",
    "                self.ts_spikes.append(t)\n",
    "                self.Vs_spikes.append(y[0])\n",
    "                self.ws_spikes.append(y[1])\n",
    "        return np.array([event_0])\n",
    "\n",
    "    #Responsible for handling the events.\n",
    "    def handle_event(self, solver, event_info):\n",
    "        \"\"\"\n",
    "        Event handling. This functions is called when Assimulo finds an event as\n",
    "        specified by the event functions.\n",
    "        \"\"\"\n",
    "        ev = event_info\n",
    "        event_info = event_info[0] # only look at the state events information.\n",
    "        if event_info[0] > 0:\n",
    "            solver.sw[0] = True\n",
    "            solver.y[0] = self.p.Vreset\n",
    "            solver.y[1] += self.p.b\n",
    "        else:\n",
    "            solver.sw[0] = False\n",
    "\n",
    "    def initialize(self, solver):\n",
    "        solver.h_sol=[]\n",
    "        solver.nq_sol=[]\n",
    "\n",
    "    def handle_result(self, solver, t, y):\n",
    "        Explicit_Problem.handle_result(self, solver, t, y)\n",
    "        # Extra output for algorithm analysis\n",
    "        if solver.report_continuously:\n",
    "           h, nq = solver.get_algorithm_data()\n",
    "           solver.h_sol.extend([h])\n",
    "           solver.nq_sol.extend([nq])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSODAR reference model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def reference_aeif(p, simtime):\n",
    "    '''\n",
    "    Reference aeif model using LSODAR.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    p : Params instance\n",
    "        Object containing the neuronal parameters.\n",
    "    f : function\n",
    "        Right-hand side function (either `rhs_aeif_old`\n",
    "        or `rhs_aeif_new`)\n",
    "    simtime : double\n",
    "        Duration of the simulation (will run between\n",
    "        0 and tmax)\n",
    "    dt : double\n",
    "        Time increment.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    t : list\n",
    "        Times at which the neuronal state was evaluated.\n",
    "    y : list\n",
    "        State values associated to the times in `t`\n",
    "    s : list\n",
    "        Spike times.\n",
    "    vs : list\n",
    "        Values of `V` just before the spike.\n",
    "    ws : list\n",
    "        Values of `w` just before the spike\n",
    "    h : list\n",
    "        List of the minimal time increment at each step.\n",
    "    '''\n",
    "    #Create an instance of the problem\n",
    "    exp_mod = Extended_Problem(p) #Create the problem\n",
    "    exp_sim = LSODAR(exp_mod) #Create the solver\n",
    "\n",
    "    exp_sim.atol=1.e-8\n",
    "    exp_sim.report_continuously = True\n",
    "    exp_sim.store_event_points = True\n",
    "\n",
    "    exp_sim.verbosity = 30\n",
    "\n",
    "    #Simulate\n",
    "    t, y = exp_sim.simulate(simtime) #Simulate 10 seconds\n",
    "    \n",
    "    return t, y, exp_mod.ts_spikes, exp_mod.Vs_spikes, exp_mod.ws_spikes, exp_sim.h_sol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set the parameters and simulate the models\n",
    "### Params (chose a dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Regular spiking\n",
    "aeif_param = {\n",
    "    'V_reset': -58.,\n",
    "    'V_peak': 0.0,\n",
    "    'V_th': -50.,\n",
    "    'I_e': 420.,\n",
    "    'g_L': 11.,\n",
    "    'tau_w': 300.,\n",
    "    'E_L': -70.,\n",
    "    'Delta_T': 2.,\n",
    "    'a': 3.,\n",
    "    'b': 0.,\n",
    "    'C_m': 200.,\n",
    "    'V_m': -70., #! must be equal to E_L\n",
    "    'w': 5., #! must be equal to 5.\n",
    "    'tau_syn_ex': 0.2\n",
    "}\n",
    "\n",
    "# Bursting\n",
    "aeif_param2 = {\n",
    "    'V_reset': -46.,\n",
    "    'V_peak': 0.0,\n",
    "    'V_th': -50.,\n",
    "    'I_e': 500.0,\n",
    "    'g_L': 10.,\n",
    "    'tau_w': 120.,\n",
    "    'E_L': -58.,\n",
    "    'Delta_T': 2.,\n",
    "    'a': 2.,\n",
    "    'b': 100.,\n",
    "    'C_m': 200.,\n",
    "    'V_m': -58., #! must be equal to E_L\n",
    "    'w': 5., #! must be equal to 5.\n",
    "}\n",
    "\n",
    "# Close to chaos (use resol < 0.005 and simtime = 200)\n",
    "aeif_param3 = {\n",
    "    'V_reset': -48.,\n",
    "    'V_peak': 0.0,\n",
    "    'V_th': -50.,\n",
    "    'I_e': 160.,\n",
    "    'g_L': 12.,\n",
    "    'tau_w': 130.,\n",
    "    'E_L': -60.,\n",
    "    'Delta_T': 2.,\n",
    "    'a': -11.,\n",
    "    'b': 30.,\n",
    "    'C_m': 100.,\n",
    "    'V_m': -60., #! must be equal to E_L\n",
    "    'w': 5., #! must be equal to 5.\n",
    "}\n",
    "\n",
    "class Params(object):\n",
    "    '''\n",
    "    Class giving access to the neuronal\n",
    "    parameters.\n",
    "    '''\n",
    "    def __init__(self):\n",
    "        self.params = aeif_param\n",
    "        self.Vpeak = aeif_param[\"V_peak\"]\n",
    "        self.Vreset = aeif_param[\"V_reset\"]\n",
    "        self.gL = aeif_param[\"g_L\"]\n",
    "        self.Cm = aeif_param[\"C_m\"]\n",
    "        self.EL = aeif_param[\"E_L\"]\n",
    "        self.DeltaT = aeif_param[\"Delta_T\"]\n",
    "        self.tau_w = aeif_param[\"tau_w\"]\n",
    "        self.a = aeif_param[\"a\"]\n",
    "        self.b = aeif_param[\"b\"]\n",
    "        self.vT = aeif_param[\"V_th\"]\n",
    "        self.Ie = aeif_param[\"I_e\"]\n",
    "    \n",
    "p = Params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulate the 3 implementations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Run Statistics: AEIF_nosyn \n",
      "\n",
      " Number of steps                       : 2013\n",
      " Number of function evaluations        : 5590\n",
      " Number of Jacobian evaluations        : 0\n",
      " Number of state function evaluations  : 2042\n",
      " Number of state events                : 7\n",
      "\n",
      "Solver options:\n",
      "\n",
      " Solver                  : LSODAR \n",
      " Absolute tolerances     : [  1.00000000e-08   1.00000000e-08]\n",
      " Relative tolerances     : 1e-06\n",
      " Starter                 : classical\n",
      "\n",
      "Simulation interval    : 0.0 - 100.0 seconds.\n",
      "Elapsed simulation time: 0.09047099999999997 seconds.\n"
     ]
    }
   ],
   "source": [
    "# Parameters of the simulation\n",
    "simtime = 100.\n",
    "resol = 0.01\n",
    "\n",
    "t_old, y_old, s_old, vs_old, ws_old, fo_old = scipy_aeif(p, rhs_aeif_old, simtime, resol)\n",
    "t_new, y_new, s_new, vs_new, ws_new, fo_new = scipy_aeif(p, rhs_aeif_new, simtime, resol)\n",
    "t_ref, y_ref, s_ref, vs_ref, ws_ref, h_ref = reference_aeif(p, simtime)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the results\n",
    "### Zoom out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X9yEbNgTywQfF3JFzB8vDlxM6OZTMzEwyMr5EcvLFjBs3F09PT6eji4iIDBmO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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feb79129cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "\n",
    "# Plot the potentials\n",
    "ax.plot(t_ref, y_ref[:,0], linestyle=\"-\", label=\"V ref.\")\n",
    "ax.plot(t_old, y_old[:,0], linestyle=\"-.\", label=\"V old\")\n",
    "ax.plot(t_new, y_new[:,0], linestyle=\"--\", label=\"V new\")\n",
    "\n",
    "# Plot the adaptation variables\n",
    "ax2.plot(t_ref, y_ref[:,1], linestyle=\"-\", c=\"k\", label=\"w ref.\")\n",
    "ax2.plot(t_old, y_old[:,1], linestyle=\"-.\", c=\"m\", label=\"w old\")\n",
    "ax2.plot(t_new, y_new[:,1], linestyle=\"--\", c=\"y\", label=\"w new\")\n",
    "\n",
    "# Show\n",
    "ax.set_xlim([0., simtime])\n",
    "ax.set_ylim([-65., 40.])\n",
    "ax.set_xlabel(\"Time (ms)\")\n",
    "ax.set_ylabel(\"V (mV)\")\n",
    "ax2.set_ylim([-20., 20.])\n",
    "ax2.set_ylabel(\"w (pA)\")\n",
    "ax.legend(loc=6)\n",
    "ax2.legend(loc=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zoom in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/DYrihslUDJ1OrzomhICvyYf+f/Qj8+ZMzZBTxa/gwDcOqKqLepse+gQ9Uj+p\nXcxICAEIcOjqaTQ2QEYLh5MNkqMBcrKBcbx2i8UCWeaCSkQzgYE0hhhIiYhosRMiDJ+vGW73Tgih\nID39WtVxr7cZgcBhJCbWnPQ+Q+8MYeD1AQy9PYSht4cAHZB8cTKKflwEvV1/0mtp+sauyDoTw1jd\nbjeMRuOUA+OJ7VarlQGSaI5iII0hBlIiIlpMhAhrFhEaGnoP+/ZdBat1FZKSNiAz80tTunfjFxsh\nxUlwnOWA/Sw74gviOY/uJIQQ8Pv905r3OLbd7XbDYDDMyPDV0e96PX+RQLQYMJDGEAMpEREtFoFA\nJ3btqsaZZ+6f1HXBniBc77kw9N4QXNtdyPpyFpI/mnzqCxeYEwPkdBfRcbvd0Ov1MzJ8dbSdAZKI\npoKBNIYYSImIaKEQQkFb2wNwu3fD52vEmjU7VXM7hRAIh72Q5Ykt6tPx2w60P9gOZUiBba0NtjNG\nvhKqEmBInvtbUQghEAgEZmwRHZfLBb1eP6Hq4kTarVYrt/QgojmBgTSGGEiJiGg+OnLkWaSkfAKy\nbFK1t7beD5NpKazWlTCbi0+6x6fiVeDe5YZkkGA/w6457jvggwgLmJaaTsvQ27FzIEeHoJ44p3Gy\nQVKn083oHMi4uLhZ/xyIiE63+R5IOTaEiIhoFgSDR+F274bNthYGQ4LqmMezF4mJH9YE0vz8/zfu\n/QKdARz78zG43nfBtd0F334fzKVmZN6YGTWQmopMUe4yYnT46tjQeGKInMixsa89Hg+MRmNkC47R\nQGixWDRh0eFwICsr65RBkgGSiGjhY4WUiIhohu3dezn6+1+F1boCy5f/BhZL6YSvDQfD0MVpK6Ou\nnS50/qYT1tVW2FbbYF1hhc6oPU8IgX379mHLli1444030N3drQmRY+c/nhggp/raYrFwDiQRUQzM\n9wopAykREdEUHThwF+z2dUhNvVzVHgoNQpbtpxwqGzwWhHunG+733XC974L7fTd0Fh3O2HXGhPvg\n8XiwY8cObNu2Ddu2bcPrr78Oh8OB6upqVFVVITc3VxMgLRYL5z8SES0QDKQxxEBKRESzyeV6H729\nL8Ht3oXU1E8gPf3TquN+/2Ho9QnQ662Tvvdw/zC2FWyDbZUN1tVWWFeNVD7NxWZIcvR/V4TDYezf\nvz8SPt955x00NjaioqIC69atw5lnnon169cjJydnSu+XiIjmHwbSGGIgJSKimeDzHUQoNACbbbWq\n/ejRv8Czxj3zAAAgAElEQVTlegcWywo4HOsRH599ynsJIeBv9cO983jVc6cbZX8qgxwvRz33ZFXU\nvr4+vPvuu5Hw+c4778DhcGDdunWRr5UrV8JoNE7+TRMR0YLAQBpDDKRERDRR4XAIPl8TwuEAbLZV\nqmO9vZsRCLQjM/OL03pG3dV16P97P3Qm3cg8z+PzPZMuSoo633Msl8uFnTt3YseOHdi+fTu2b9+O\nrq4urF27NhI+zzzzTKSnp0+rj0REtLAwkMYQAykREU1Ef/8rqK29FEZjNtLTP438/HsnfY/hgWF4\ndnvg3uVGysdTEJ8XrzlnaPsQjNlGGJecvGLpdruxa9cubN++PRJA29vbUVFRgbVr12LNmjVYs2YN\nysrKIMvayioREdEoBtIYYiAlIqKxPJ46HDhwJyor/6pqVxQ/AAWybJnU/br/sxvH/ucY3LvcGD42\nDEulBdaVVmR/LRvm5eYJ9smDXbt2RYLnjh070NLSgoqKCqxZsyYSQEtLS7nQEBERTRoDaQwxkBIR\nLS6hkBuHD/8MHs8eDA/3YeXKf6iOh8MBDA/3wmjMnND9woEwPHUe6BP0MBVq9+3s/WsvFI8C60or\nTEUmSLqT//fe6/Vi9+7dqsrnwYMHUVZWFgmfa9euRVlZGcMnERHNCAbSGGIgJSJamIRQ0NX1GDIy\nblQt+hMOB9HS8h1YrZWwWCphtZZP6r6eBg/6XuqDe7cb7l1u+Pb7YFpqQu49uUi/enJzM91uN/bs\n2YOdO3dGAmhzczOcTmek6rl27VqUl5cjLi5uUvcmIiKaKAbSGGIgJSKav8LhYfh8TXC79yA19RPQ\n6dTzLvfv/zoKCx+CLGvnap6MUARCQyEYErUVyN6/9qJvcx+sK6ywrrTCXGaOuvrtiY4ePYqdO3di\n586d2LVrF3bu3In29naUlpZi1apVkQBaUVHBFW+JiOi0YiCNIQZSIqK5b+TvaQFJUq8y+957FQiH\ng7BYKrB8+a8RF5c26XsrHgWu7S6497jh2eOBe7cbnjoP0q5OQ8ljJVPqa2trayR0jn653W6sXLkS\nq1atwqpVq7By5Uo4nU4OuyUiophjII0hBlIiorkl2r6au3Z9GAUF98HhOFvVHg4PQ6ebWKAbb7/O\noe1DaP5qM6wrrCMLDlVaYSm3QO/Qn/KeoVAIDQ0NquC5a9cumM3mSPAcDZ8FBQUn3S+UiIgoVhhI\nY4iBlIgo9jo7H0FPzx/h8ezF8uW/RmrqJ1THFcU/qWG3wZ7gBxXP49/DwTA+tPdDU+6j1+uNzPcc\n/dq3bx+ys7M14TMtbfKVWiIiolhhII0hBlIiotOnp+eP0OsTkZT0YVV7f/8WhMN+WCzlMBqzplVJ\nDA2FsC1/2wfVztHvZRbIlontx9nb26upera2tsLpdKqC54oVK2C1WqfcVyIiormAgTSGGEiJiGZO\nINCNoaFt8Hj2wmqtRErKZarjg4NvQpZtsForJ3VfIQSCXSdUPWs9WP3W6qghc7zhuSdSFAX79+/H\n7t27I1979uzB0NBQ1PmeXOmWiIgWIgbSGGIgJSKavOHhfoRCfTCZilTtR448hyNHnoPFUo6UlI/B\n4ThnRp733sr3EOwIwrLihKpnpQU6ve7UNwAwMDCAPXv2qMLnvn37kJGRgRUrVqCyshIrVqzAihUr\nkJ+fz/meRES0aEQLpJIkPQbgUgBHhBCVx9tWAPgtgHgAwwBuEUJsP+G6FQB+A8AGQAHwgBDi+Vnt\n/3wOdAykRETjGx7ux/BwL8zmpar23t6/YnDwLRQWfm/K9xZhAX+LH569HrhrRyqfed/Jg7VcOwR2\neGAYeod+QiExHA6jublZEz77+vpQUVGhCp8VFRWw2WxTfg9EREQLwTiBdD0AN4CnxwTSlwH8RAjx\nd0mSPgrgm0KI8064bikAIYQ4IElSBoAdAEqEEEOz1f9TL0NIRETzisu1A7W1l0FRhpCWdg2Kix9V\nHU9OvhjJyRdP+f77v74fXb/rgiHJAEuFBZZyC5IvS4YxI/r+m4aE6CvpDg0NRYLn6Pe9e/ciNTU1\nUu383Oc+hxUrVqCwsBA63cSqqURERIudEOINSZLyTmgOA3Ac/zkBQEeU65rH/NwlSVIPgFQAsxZI\nWSElIpqnfL5W7N9/Kyor/1fVriheBIM9iI/P1ez9eTIhdwjeOu9IxbPWg+RLk5H0kSTNef42P/QJ\n+gltrQKMVD1bWlpU8zx3796Nnp4elJWVRcJnZWUlKisr4XA4Tn1TIiIiAjD+HNLjgXTTmAppCYCX\nAUjHv84WQhw6yX0/BOAJIUTZ7PR8BCukRERzlKL40N7+IDyeOgSD3Vi9+k3VcaMxE0VFP9ZcJ8tm\nmEz5E35O1+NdaPteG4LdQZid5kjV05gTveIZnzf+Fi5utxu1tbWq8FlbW4uEhIRI8Lz22mvx0EMP\nYenSpZDlia2cS0RERCO2bNmCLVu2TOXSmwF8TQjxF0mSrgDwOICPRDvx+HDdpwFcN9V+ThQrpERE\nMSaEQFvb95CX9y1IkjymPYzW1u/CYimF2VwGi6VsUov1CCEQaA9EKp7xBfFIvzpdc56/zY9wIAxT\nkQmSPLH7j/S5TbPCbUdHB0pLSzVVz6QkbaWViIiIpm8SFdIBIUTCmOODQgjNsCRJkmwAtgD4vhDi\nf2av5yNYISUiOg283v3weGrh8exFdvZt0Os/WIxHkiRIkgHhcACybB7TrkNBwXcn/azBtwZx4I4D\n8Oz1QLbJkYqn7YzoCwCdrOIJjKxwu3fvXtTW1ka+9uzZA6vVGgmeV155Je6//34sX74cej3/00JE\nRDQHjA7NHdUhSVK1EGKrJEkXAGjSXCBJBgB/AfDU6QijACukREQzyudrQVxcGmTZomrfs+cSSJIM\ni6UMOTl3wGBIntL9Fa8Czz4PPLUeiJBA5o2ZmnMC3QH4mnywlFtgSIq+oFA0wWAQDQ0NquBZW1uL\nvr4+lJWVoaKiAhUVFaisrERFRQVSUlKm9B6IiIho5oyzyu5zAGoAJAM4AuBeAI0A/h2ADMCPkW1f\ndkqStAbAl4QQX5Qk6dMYGcpbh5EwKwBcL4TYM2v9n8+BjoGUiGJBiDD8/lbo9UkwGBJUx+rrr0N2\n9tdhs62ZsecFOgPYf+t+eGo9CHQEYFpugqXcgoSqBGR+URtIT0UIgfb2dk3wbG5uRn5+fiR4jn4V\nFBRwhVsiIqI5arwhu/MFAykR0SQcOHAnOjp+A4MhCSUlTyAx8YJp3W90nqenzgN/qx9Zt2RpzlG8\nCnr/txeWcgtMy0zQGSYeDgcGBlTDbGtra7F3715YLBZV6KysrITT6UR8/MmH7xIREdHcwkAaQwyk\nRDRb2toeQFzcEmRk3KBq9/sPQ6+3Q6+3T/neIizQ9KUmuGvd8O7zjszzLBuZ51n04yJIusn/NyUQ\nCEQdbjswMIDy8nJN1TM5eWpDhomIiGhuYSCNIQZSIpqqgYHX0N39NLzeOqSkfAK5uXeqjgcC3ZBl\n85SCZ/BoEJ69HnjqPMj4fAZki3Zrk+6nuxFfGA9LmQWGxInP8xxd3fbEBYYOHjyIgoICTfDMz8/n\ncFsiIqIFjIE0hhhIiehUXK4d8PkOIi3tSlX74OA2uN27YLGUwWKp0MwFnazW+1ox8OoAPHUjiw2N\nVjzz78tHXGrclO7Z19enqXju3bsXdrtdEzxLSko43JaIiGgRYiCNIQZSIgqFBjE4+Ba83n2QZTsy\nM29UHXe7a+H3tyEl5dKpP2MoBM8+D7x1XiRckABTvklzztE/H4VsHRl6G5cRN6n9QgOBAOrr6zXh\nc2hoKOpwW+7pSURERKMYSGOIgZRo8QiFXPB662G3f0jVPjT0Hlpa7oHZXIqEhGqkpl4+I8/reqIL\nR/94FJ46D4aPDcPsNMNSZkHOHTmwVlindE9FUdDS0oK9e/eq9vVsaWlBYWGhakuViooK5OXlTSrY\nEhER0eLDQBpDDKREC0s4HILffwCBQCcSE89THfP5DqC9/SEUF//HtJ+j+BX4Gn3w7PXAXGKGbY1N\nc07/K/1QXAos5RbE58dDkif+97wQAocOHUJdXV0kfO7duxcNDQ1ITU1FeXl55Gt0uK3RaJz2+yIi\nIqLFh4E0hhhIieancDgEnU6vagsGj2HbthzExWXCbj8TpaXPzegz+/7eh85HOiPbq5gKR/byzPjX\nDCRdOLUhsEII9PT0YO/evarwWVdXB4vFgvLycpSVlUXCZ2lpKWw2bfglIiIimioG0hhiICWa2xTF\nD53OqBp2Gg4P46230nD22T3Q6T5YXVYIgXDYB1k2T/o5QhHwHfDBU+eBbJOR9GFtwHS974K3yQtL\nuQXm5Wbo4ia38mx/f78mdO7duxfhcFhV8SwrK0NZWRm3VSEiIqLTgoE0hhhIieamffs+A5frHfj9\nh3D22R0wGNThLBwOQqeb2sqzo9y1brQ/1A5PnQe+Jh/ilsTBUmZByidTkHF9xpTv6/F4sG/fPtVQ\n27q6OgwNDaG0tFQTPpcsWcJ5nkRERBQzDKQxxEBKFFuNjV9Ebu6/wWQqULX39/8TcXGZMJmWqqqg\nExUeDsPX7IN3nxdhfxjpn07XnONv92NgywAsZRaYS8xR9/o8mUAggIaGBs08z+7ubpSUlKiG2paX\nlyMnJ4f7eRIREdGcw0AaQwykRLPr6NG/YHDwNXg8+1BY+H3YbGtUxwcH34bFUg69fvrzIgPdATR/\ntRmefR74D/hhzDbCXGqG41wHcu/InfJ9Q6EQmpubNfM8W1tbUVhYqKp2lpeXo6ioCLI8uXBLRERE\nFCsMpDHEQEo0M3p7X0J8fD4sljJVe0fHb6EoLpjNTjgc62EwJEzp/uFAGN79XnjrvPC3+5F7pzZg\nKl4Fx/58DOYyM8zFZsimyYXCcDiMtrY2zVDbpqYmZGZmaobaLl++nCvbEhER0bzHQBpDDKREExMM\nHoHbvQseTz3s9jPhcJylOn7kyO9hNi/TVECnQ4QF6q6qg2fv8VVtC0wwl47s5Zn/3fwpz7sUQuDw\n4cPYt2+fquJZX1+PxMREzcq2TqcTZvPkF0oiIiIimg8YSGOIgZRILRDoQjjsg8lUqGo/dOhh9Pa+\nBLPZifT0T2sC6WQpPgXexpGKp2efB7l350Jv02vOO/biMcQXxsO8zAydcXLzL0eDZ11dXSR8jv5s\nsVhQVlamWmSorKwMDodjWu+LiIiIaL5hII0hBlJajBTFC6+3EZIkw2qtVB07cuRZhEIDyMr68qw8\nu+nmJvT9Xx+CHUGYlh6veJZakPXVLBgSJ794ETASPA8dOqQJnWOD52j4HP2elDS1fUOJiIiIFhoG\n0hhiIKWFLBwOQKdTz3Hs6flvNDRcB5NpKZYs+Txycm6fkWeF3CF4Gz6oeC65fgksTovmvME3B6FP\n1sNUZILOMPmKZ3t7eyR4jn6vr6+H1WrVhE4GTyIiIqJTYyCNIQZSmu+EEFCUIej16qGmfX3/h46O\nf0dFxSZVezgcACBDp9MOj52K1u+2ouuJLgz3DMO03ARLmQWWUgvSPp0GU75pSvccDZ5jQ+doxdNm\ns0WteCYmJs7I+yEiIiJabBhIZ5EkSRcB+BkAHYDHhBA/OOE4AynNSz7fAezbdy283gbYbKuxcuWr\nquNCKAB0U174Z3hgGN56L7z7RiqeSRcmIWmDttro2eeBZJBgKjRBkif3rHA4PG7F0263R614MngS\nERERzSwG0lkiSZIOQBOACwB0AngPwNVCiIYx5zCQ0pwWDPagoeFzqKz8m6pdUbxwud6HxeKEwZA8\nY8/r/I9OtN7bCsWlwFxihtk5sqpt0iVJsJZbp3TP0eB54uJC9fX1cDgcmoqn0+lk8CQiIiI6TRhI\nZ4kkSesA3CuE+Ojx13cDEGOrpAykFGvh8DBaWr4Nr3cf/P42rF27CyO/SxkhhAK3ew9stlVTfoYI\nCwQOBeCp98C7zwtvvRfWlVZkfTlLc26gMwAREjDmGCddXR3dx/PExYXq6+uRkJAQteKZkDC1fUmJ\niIiIaGbM90A6MxPRZkcWgENjXh8G8KEY9YUIzc23o6DgAchyfKRNkvQwGFKxZMkNMJudANR/F0iS\nPK0weux/j2Hf1fugT9DD4rTA7DTDusYKx9nRtzcxZhqjto8VDofR2tqqGWrb0NCAxMTESOg899xz\ncdNNN8HpdDJ4EhEREdGsmMuBNFrKZzmUZk1f3z/gdu+C11uPwsIHEReXpjpusZQDUFRtkiQhN/eO\nST1H8SvwNfpGKp7H53kaUgxY/pvlmnMTz0vE2R1nQ++Y/B/V0eB54uJCo8FztNJZVVWFm266CaWl\npdzHk2iBkSTg5ZeBCy+MdU+IiIiim8uB9DCA3DGvszEyl1Rl48aNkZ9rampQU1Mz2/2iea6v72VY\nras0gbO3dxMACXb7hyBJcZrrMjJumPaz3Xvd2LF2B0yFx/fwdFqQcnkKrCuiz++ULfIp7xkOh9HS\n0hK14pmcnBypeFZXV+OWW25BaWkp7Hb7tN8LEc0P77/PQEpERHPXXJ5DKgNoxMiiRl0A3gVwjRCi\nfsw5nENKGsPDffB66+H1NiAhoQYmU5HqeFvb95GS8nFYLGUz8rzg0SC89SOr2XrrR+Z4hgZCWPPu\nGs25QhEQYTHpPTwBIBQKRYJnfX19ZJ5nY2MjkpOTo87xZPAkWtwkCXjwQeDuu2PdEyIimi2cQzpL\nhBCKJEm3Avg7Ptj2pf4Ul9EiEgh0QpL0mkpnS8s9cLneh9lcAqtVGwrz8u6Z9LOEEFEXCVK8Ct4t\neRfm4uMr2pZakLQhCWanOep9JFk65fYqgUAA+/fvR319fWT/zvr6euzfvx9LlixBaWkpnE4nzjvv\nPNx6661wOp0MnkREREQ0L83ZCulEsEK68CmKF15vE/R6O0ymQtWxtrbvw2RairS0T83oM73N3shq\ntpGqZ4MXZx0+C3q79nc444XVU/F4PGhsbFSFzn379qGtrQ35+flwOp2R8FlaWori4mJYLJaZeItE\ntEiwQkpEtPCxQko0TUIICBGETqdeIfbQoYfR0nIPTKalyMm5UxNIp1LpHBUOhAEZ0Om1Q2cb/7UR\nslmG2WmG41wHMr+UCbPTHDWMAjhlGB0YGEB9fb2m4tnd3Y1ly5ZFQue1116L0tJSLF26FEbjqVfL\nJSIiIiKa71ghpdMmHA5BUYZgMCSp2js7H4HHsw/Llv1c1a4oXkhSHHS66f3exF3rhnune6TieXwv\nT3+7H6vfWg3batu07j1KCIGjR4+qKp2j34eGhuB0OiOVztEAWlBQAL2evxMiotnDCikR0cLHCinR\nKQwMvIamppvh9x9Eevp1KC5+VHU8I+NfMbKGlZosR5+HeSIhBIJHgpBNctTtUbqf6EawKwhzqRnp\nn0mHxWmBaZkJurjJLywkhEBHR4dmmG19fT0URYkEztLSUlxyySUoLS1FdnY2dLrJP4uIiIiIaKFj\nhZRmjMu1A+3tP0JZ2e9V7cPDvQgEOmAyLYMsm6b9nKF3hzCwZSAyt9NT74Gkl1DyeAlSLkuZ9v0B\nQFEUtLa2akJnfX09zGazqtI5+j09PX1Kc0mJiGYLK6RERAsfK6S0aAQCXTh48C54vQ2QJANWr35T\nddxsdqKo6Eea6wyGZBgMyRN+juJR4G30QrbLMC/VVkm99V4Eu4Own23HkhuWwOw0Iy5Fu2/oRAwP\nD6O5uVlT8WxqakJqamokbK5fvx433ngjnE4nkpKSTn1jIiIiIiI6JQZS0giFBtHc/HWUlDyhapdl\nGxISzkNm5s0wm4s118myecLDbMcaencIR547Eql4DvcMw7TMhJxv5EQNpEs+t2TSz/D5fJEVbcdW\nPA8ePIicnJxIxfOiiy7C7bffjpKSElit1kk/h4iIiIiiy8/PR1tbW6y7MW/l5eWhtbU11t2YcQyk\ni5AQYXR0/AJebwN8vgOorNwMSfpgjqMs25Cc/DHNdiZ6vRUZGZ+f3LMUAV+LD94GL2SzjMTzE6Oe\nY8w2IukjSTCXmBGfH3/KvTrH09/fj4aGhsjw2tGvzs5OFBUVRSqeV111FZxOJ5YvX474+PgpPYuI\niIiIJq6trQ2cbjd1C3VqGAPpAtfW9iCys7+umrspSToEAh0wm8uQmnoFAPVfDJKkQ2rqJ6b8THet\nG233t8Fb74Wv2QdDugEWpwUpH0+JGkgdZzngOMsx4fuPLiw0GjbHBlCPx4OSkpLIqrZf+MIXUFpa\nisLCQhgMhim/JyIiIiIimnkMpPPc4OCbcLv3wOttRG7u3TAa1cNZdbp4hMMBzWJCRUU/nPSzhBAY\nPjYcGVorQgJZt2RpzjMkGZByeQrM3zLDvNwM2axdQXcihoeHceDAAU3Fs6GhARaLBU6nMxI+L7/8\ncjidTmRlZS3Y3x4RES0mQggElSCMeu7LTES0kDGQzhN9fS/DYlmhCZxHjjwHIYIwm0sgSdr/OXNy\nbpv2s/2H/dh39T54672AAMxOM8xOM+xn2qOeb8wyIv2a9Anf3+12o7GxUVPxPHjwILKzsyOhs6am\nBjfffDNKSkqQmKittBIR0cLR6+tF6o9S0fDlBhSnaNctICKihYGBdI4IBo/A46mD19uIxMQLYDYv\nVx13u3chLi5LE0iXL//VlJ+p+BT4mnzw1HvgbfAi2BlE8aPa/+jHpcah8IFCmEvMMKQaplSBFELg\n6NGjUYfZHjt2DMuWLYsMs+X8TiIiGgoMId2SjmXJy2LdFSKiGfHtb38bjzzyCAwGAzo7O2PdnTmD\ngfQ08/kOQqczwmhUD3Vtb38ILtcOmM3FsNvP0lyXm3vXjPVBKALvlryLwOEATEtNMJeMVDwTahI0\nCxkBgM6oQ0JVwoTurSgK2traNKGzoaEBQohI6CwpKcFHPvIROJ1O5OXlQZanNqyXiIgWJk/QgxxH\nDnRjFt0jIpoNF110EdatW4eNGzeq2l944QXcdNNN6OjogE43vb+LDh8+jIcffhiHDh1CcvLEt0Nc\nDKT5vNKVJElirvVfCIFgsAtebwPi4pbAYilVHT906GHEx+dPa9GgaMKhMPytfvgaR1a0Hf0qf6Ec\nhiTtYj6+Fh+MOUbo9FP7w+X3+7F//37N3M6mpiakpKSoFhYaDaBpaWmc30lEdBpJEvDgg8Ddd8e6\nJ0REI6vEzrV/uwPA73//e9xzzz04cOCAqv3KK69EQUEBfvjDU6+9Eq2oM9Ybb7yBa6+9Fu3t7VPu\n53if3/H2efuPbAbSKVIUP8JhDwwG9W842toexOHDP4PZXIysrFuRlnbVaenPeyveQ2gwNFLtLDHD\nXDxS9XSc5YDOOPXf6AwMDEQdZnv48GEUFBRoQif37yQimjsYSIloLpmrgdTv9yMjIwObNm3C+vXr\nAYz8GzgjIwPvvfceysvLNdecd955OOecc7Blyxbs3LkTtbW1SElJwW233Ya//e1vkGUZ119/Pe67\n7z688sor+NjHPoZgMAiz2YwrrrgCjz/++KT7uVADKYfsnsRotTMc9sFkKlId6+l5Dj7fARQWfl/V\nnpv7TeTl/du0n+0/7Ie3zquqdnobvSj9QykSztUOn12zfQ10hqkFz7HbqJy4ou3YbVRKSkpwww03\nwOl0oqioiNuoEBEREdG8Fx8fjyuvvBJPP/10JJD+4Q9/gNPpjBpGRz3zzDPYvHkzli9fjnA4jCuu\nuAKZmZk4ePAg3G43Lr30UuTm5uLGG2/E3/72N1x33XXTqpAuVAykAIQIQzphjkpf38uoq7sSOp0J\nGRk3oLDwQdXxjIwbot5LkiY+FzLkDgEA9Fbt/wxt97fBd8A3Uu0sMyPlEykwl5hhzIq+/P1Ewmi0\nbVQaGhrQ0NAAs9ms2UalpKQE2dnZHGZLREQxccXzV+DzKz+PS5ZfEuuuENEC97nPfQ6XXHIJfvGL\nX8BoNOI///M/8bnPfe6k11x//fUoKSkBABw7dgybN2/G4OAgjEYj4uPj8fWvfx2PPvoobrzxxtPx\nFuatRRNIhRAIhQZhMKiri4ODb6Kl5V6sXPkPVbvDsR7r1rXBYJiZ7UXce9wYfH1QVe0cPjaMkidK\nkPapNM35xY9MfYn7k22jkpWVFRliW1NTg5tuuglOp5PbqBAR0ZwSCAUQVILwh/yx7goRnSYzVQOZ\nyqjgc845B2lpaXjhhRdwxhlnYPv27fjzn/980mtycnIiP7e1tWF4eBgZGRnH+yAghEBubu7kO7PI\nLPhA6ve3oa7uCni9jbBYKrB69Zuq4zbbmais3Ky5TpYtkGXLhJ+j+BT49vsgW2SYikya467tLrhr\n3TCXmJF0cRLMJWbE58ZDkqf2J08Iga6urkjYHK10NjQ0oLe3F8uXL0dxcTG3USEionnpR2/9CJua\nNuGa8mti3RUiOk1iPb30uuuuw1NPPYWGhgZceOGFSE1NPen5Y0cR5uTkID4+Hr29vRxdOEkLJpAG\ng8dQW3sp1qzZpmo3GNKxdOkvYDYXR6126nRT+wiGtg+h59meSMUz2B1EfGE8sm/LjhpIM27IQMYN\nGZN+TjAYxIEDBzShs6GhASaTKbKQUElJCS6++GI4nU7k5uZOe2lqIiKiWPp21bdxx9l3wKDjegVE\ndHp89rOfxfe+9z3U1tbipz/96aSuXbJkCS688ELcdtttuP/++2G1WtHS0oLDhw+jqqpqlnq8MCyY\nQGowJMHp/E9NuyzHw+FYN6l7hYNh+JpHtk+RLTKSNiRpT1KAuKw4JJyfMFLtLIif8hYqANDf368J\nnA0NDWhra0Nubm4kdNbU1ODmm29GcXExkpKi9IuIiGiBiNdzVA8RnT55eXk4++yzUVtbi8suu+yk\n50argj799NO46667UFpaCrfbjcLCQtx1113j3sNms2Hz5s0455xzpt33+WxC275IkpQG4BwAmQB8\nAPYC2C6ECM9u907Zrxnb9sW9242Wb7fA2+CF/5Af8XnxMBebkfyxZGTemDkjzwiHw2hvb48aPEdX\ns6HDrZIAACAASURBVB27om1JSQmKiopgNEZfyIiIiOhkuO0LEc0lc3Xbl/liUW77IknSeQDuBpAE\nYCeAHgDxAD4OoEiSpP8G8BMhxNBsd3SqwqEw/Af98DaODK2FAHK/qZ1cbEg3YMkXlsBcbIapyARd\n3NSrnT6fD01NTZrQ2dTUhMTExEjorKiowJVXXomSkhJkZmZyvDkREREREc070siWJSvwQQGzTghx\nZCLXnmrI7sUAbhRCaDbMkSRJD+BSAB8B8KdJ9fg08B/yY8+Fe+Br8cGYaRzZPqXEDNtaW9TzjUuM\nSP34yScujyWEwJEjR9DQ0IDGxsbI9/r6enR3d6OoqChS5bzkkkvwjW98A8XFxbDZoj+fiIiIPuAJ\nemAymKCTuCYCEdFcJUlSEYC7AHwYwH4ARzFSwFwuSZIXwCMAnjrZyNqTDtmVJCl9osk2FiRJEns+\ntgcVL1ZojoWHw/A2eGFaZoIcP/G9QU8UDAbR3NysCp6jP+v1+kjoLC4ujqxqW1BQAL1+wUzPJSKi\neWo+D9l1/sqJVUtWoSy1DPdU3RPr7hDRDOCQ3emZi0N2JUn6LwC/AfD6/2fvzuOjqs4/jn+ee2dJ\nAgQI+44sioAiCC5sxh21qK0bLoBLtbWttdXaav1VoS7V1i7W1qp1qVrE3aLWVrEVVBQVUVEBFYuA\nrJGwhCyznt8fNwRCEogkYZLwfb9e9zUz555z55kJd4Znzrnn7HgtpZl1As4GNjjnHqzpGLvKmj4w\nsw+B6cBTzrlNdYy53nX/UXecc1WGu3phj5YHtKz1cb766qtKyebW+ytWrKg0qdDYsWO55JJL2G+/\n/Wjfvn19vxwREREBNsc2c+NRN9Ijt8euK4uISEY453a2Nlehc+4PuzrGrhLSbgTdrxOAX5nZmwTJ\n6bPOudJaR9qA2h5VdSmXmiQSCZYuXVpt4plKpSotoXLhhRdWTCoUiUQa8BWIiIjIjpxztMtuR9jX\nsi8iIk2FBb2ERwLnAOOBTrtsU9tuczOLACcQJKdHAv9xzp2729HWg5pm2d2wYUOV4bWLFy9m6dKl\ndOvWrWKI7fa3HTt21KRCIiLSrDTlIbsi0vxoyG7dNMYhu9vFcChBEvpNgglxv0/QiblhV21rfaGj\ncy5uZguBRcDBwMDdC7d+vfDCC1USz5KSkkrXdp577rkMGDCAfv36kZWlNc1ERERERETqysxuAs4E\nlhOMpP0lwfKgNV4zuqNdJqRm1hM4i+CC1BbAo8ApzrlFuxN0fbv99tsZMGAABx10EBMmTGDAgAF0\n6dJFvZ0iIiIiIiIN6xLgE4KJjZ53zpWZ2dfqBt/VOqRvEFxH+gRwiXNu3u5G2lBefPHFTIcgIiIi\nDWD6h9OZ+b+Z3H/K/ZkORUSk1qZOncqSJUt4+OGHq92/zz77cN9993HUUUft4cgaRGfgOILOyz+Y\n2StAtpmFnHPJ2hxgV4t7XQP0ds79pDEmoyIiItL8JFIJiuPFeOZRnCjOdDgi0syNGzeOKVOmVCmf\nMWMGXbp0IZ2ucQnNGu0tozWdcynn3L+cc5OAfsAM4E1gpZk9Uptj7DQhdc7Nds45M9vHzH5nZk+b\n2bNbt7q/BBEREZHK5qyYw4SnJhDxI8RT8UyHIyLN3Pnnn19tb+bf//53Jk6ciOftqg+v8TGz+8xs\nrZkt2K5siJm9aWbvmdnbZja8hraTzexTM/vEzCbV9jmdc2XOuSedc98iSE5rNZS1tu/uP4AvgDuA\n3263iYiIiNSr/N75PHf2c4zfbzyPnf5YpsMRkWbu1FNPpbCwkNdff72ibOPGjTz//PNMmlR9PrZ6\n9WpOOeUU2rVrx7777su9995b4/EffvhhevfuTYcOHbj55pvrPf4aPAAcv0PZr4HrnXNDgeuB3+zY\nyMzaAtcBI4BDgevNrPWunszM2pnZHWY238zeBW4Enq9NoLVNSMucc390zr1S3ms62zk3u5ZtRURE\nRL62kBci4mstcBFpWFlZWZxxxhk89NBDFWWPPfYY+++/P4MHD662zYQJE+jZsydr1qzhiSee4Oc/\n/zmvvPJKlXoLFy7ke9/7HtOmTWPVqlWsX7+elStXNthr2co59zqw45IraWBrctkGqC6Q44GXnHOb\nnHMbgZeAcbV4ykeBdcBpwOlAAVCrXxRrm5DebmbXm9nhZjZs61bLtiIiIiIiIo3W5MmTefzxx4nF\nYkDQqzl58uRq63755Ze88cYb3HrrrYTDYYYMGcK3v/3taof9PvXUU4wfP55Ro0YRDoe54YYbMnl9\n6Y+B28xsOUFv6TXV1OkGrNju8crysl3Jc87d4JxbWr7dSJD07lJt1yE9AJgIHEWQWQO48sciIiIi\nIiJ1MmXWFKbOngrA9Udcz5T8KVX2A9WW76xdbYwaNYqOHTsyY8YMRowYwbx583jmmWeqrbtq1Sry\n8vLIycmpKOvVqxfvvvtutXV79OhR8TgnJ4d27dp97fi2N2vWLGbNmrU7TS8FLnfO/cPMTgfuB47d\noU512XJtlnF5xcwmAI+XPz4d+GdtgqptQvpNoI9zTjMLiIiISIPaEt9CxI9ouK7IXmZK/pSdJpM1\n7dtVu9qaOHEiDz74IIsXL+a4446jQ4cO1dbr2rUrhYWFFBcX06JFCwCWL19Ot25VOxK7dOnC4sWL\nKx6XlJSwfv36OsWZn59Pfn5+xeOpU6fWtulk59zlAM65J83svmrqfAnkb/e4O1B1LHJV3wGuALZ2\nE/tAsZldETydy62pYW2H7H5ALbtcRUREROriyhev5P737mdhwUKG3a0rhERkz5g0aRIvv/wy9957\nb43DdQG6d+/OyJEjueaaa4jFYixYsID77ruP8847r0rd008/neeff5433niDRCLBddddh3O16XCs\nF0blHs+VZnYEgJkdDXxaTZsXgWPNrHX5BEfHUovZcp1zrZxznnMuXL555WWtdpaMQu0T0k7AYjN7\nUcu+iIiISEPaHN9MbjSXAe0H8MZFb2Q6HBHZS/Tq1YuRI0dSUlLCySefvNO606dPZ+nSpXTt2pXT\nTjuNG264gaOOqno148CBA/nzn//M2WefTdeuXWnXrh3du3dvqJdQoXwN0DeAfc1suZldAFwM/NbM\n3iOYBfeS8roHm9k9AM65DcANwDzgLWBq+eRGNT1P713EYWa20xdstcnQt2bSO8r0TLtm5vbgLwwi\nIiJNihn86ldw9dWZjuTrOfups5l04CRO6H9CpkMRkXpkZnuyd7DZqen9Ky/PyExJZvYEQSfnDOBd\ngtl1swjWIT0SOJpgqZmZNR1jp9eQWnnGt7PE05QVioiISD2aftr0TIcgIiK14Jw7w8wGAucCFwJd\ngBJgEfACcJNzrmxnx9jVpEavmNlTwAzn3PKthWYWAUYDkwkucv3b7r4IERERERERaZqccwuBa3e3\n/a4S0nEEme50M9sH2EjQBesTLJL6e+fc+7v75CIiIiIiIrL32mlCWt69eidwp5mFgfZA6c4ubBUR\nERGpD7FkjPa/aU/RNUWZDkVERBpIbWfZxTmXcM6tVjIqIiIiDcU5R0FxAc45wn6YLfEtmgRFRKQZ\nq3VCKiIiItLQ4qk4Q+4agpnhmUfIC5FMJzMdloiI7ISZPWxmF5vZgK/bVgmpiIiINBrRUJRVV66q\neFx0TREhb1dTXoiISIY9QDDD7h1m9rmZPWVml9em4U4TUjP7k5mNrI8IRURERL6urFAWZhlZXk9E\nRGrJOfdf4CbgF8C9wHDg0tq03dVPjp8BvzWzLsBjwHTNqisiIiIiIiJbmdl/gBbAm8BrwAjn3Lra\ntN1pD6lz7nbn3OHAEUAh8ICZLTKz68xs3zrGLSIiIiIiklHjxo1jypQpVcpnzJhBly5dSKfTez6o\npmcBEAcGAwcCg80suzYNa3UNqXNumXPuVufcUOAc4JvAot0MVkRERKRapYlSNsc2ZzoMEdmLnH/+\n+Tz88MNVyv/+978zceJEPE/T7uyKc+7HzrmxBHnieoJrSmu1Okut3l0zC5vZeDObBvwL+BQ4bTfj\nFREREanW04ue5rvPf7fi8dC7h7KwYGEGIxKR5u7UU0+lsLCQ119/vaJs48aNPP/880yaNKnaNkce\neSTXXXcdo0ePJjc3l3HjxlFYWFixf+7cuYwaNYq2bdsydOhQZs+eDcCsWbM48MADK+odc8wxHHro\noRWPx4wZw7PPPlvfL7HBmdkPzOwx4H3gVOB+4ITatN3VpEbHmtn9wJfAJcALQF/n3FnOuX/ULWwR\nERGRyjbHNpMbza14PPeiuezffv8MRiQizV1WVhZnnHEGDz30UEXZY489xv7778/gwYNrbDd9+nQe\nfPBBCgoKiMVi3HbbbQCsXLmSb3zjG1x33XVs2LCB2267jdNOO43169dz+OGH8/nnn1NYWEgqleLj\njz9m5cqVFBcXU1ZWxvz58xkzZkyDv+YGkA38DhjgnDvaOTe1fKKjXdpVD+nPCS5M3d85N945N805\nV1zHYEVERESq5XB0btm54nE0FNUsuyLS4CZPnszjjz9OLBYD4OGHH2by5Mk7bXPBBRfQt29fotEo\nZ555Ju+/H8z9Om3aNE466SSOP/54AI4++miGDx/OCy+8QDQaZfjw4bz66qvMmzePAw88kNGjRzNn\nzhzmzp1L//79adu2bcO+2AbgnPuNc+4t59zXXjh6V5MaHemc+6tzrnBn9URERKRxamq53PdGfI8p\n+VMyHYaIZMKUKcGH1o5bNRMO1Vi/prq7MGrUKDp27MiMGTNYunQp8+bN45xzztlpm86dt/14lpOT\nw5YtWwBYtmwZjz/+OHl5eeTl5dG2bVvmzJnD6tWrARg7diyvvPIKr776Kvn5+eTn5zNr1ixmz57N\nEUccsVvxN2UZW2nazH4NjAdiwOfABc65zeX7rgEuBJLA5c65lzIVp4iIiIiI7AFTpny9hPLr1t+F\niRMn8uCDD7J48WKOO+44OnTosFvH6dGjB5MmTeLuu++udv8RRxzBlVdeSa9evbj66qtp06YNF198\nMVlZWXz/+9+vy0tokjI5ZdRLwCDn3EEE651eA2BmA4Ezgf0JLoS90zRWR0REREREGtCkSZN4+eWX\nuffee3c5XHdnzjvvPJ577jleeukl0uk0ZWVlzJ49m1WrVgEwcuRIPvnkE95++20OOeQQBg4cyLJl\ny3jrrbcYO3Zsfb2cJiNjCalz7mXn3NZFfeYC3cvvnww86pxLOue+IEhWD8lAiCIiIk1eU/9J9/TH\nT2fG4hmZDkNE9gK9evVi5MiRlJSUcPLJJ++07s76y7p3786MGTO4+eab6dChA7169eK2226rWM80\nJyeHgw8+mMGDBxMKBQNWDz/8cHr37k379u3r7wU1Eeacy3QMmNmzwHTn3HQzuwN40zn3SPm+e4EX\nnHNPV9PONYb4RUREGiMzuPVW+OlPMx1J7RUUF9A6qzURPwLAGU+cwZkDz+SMQWdkODIRqSszQ/93\n3301vX/l5U3258cGvYbUzGYCnbYvAhxwrXPuufI61wIJ59z07ersqMZ/uVO2Gze+9aJgERERCTS1\nHtILZlzA1PypHNz1YAAifoR4Kp7hqEREpKFktIfUzCYTrG96lHMuVl52NeCcc7eWP/43cL1z7q1q\n2quHVEREpAZm8Otfw1VXZTqS3RdPxQl5ITzL5LQXIlIf1ENaN821hzRjn+5mNg74KXDy1mS03LPA\nBDOLmNk+QD/g7UzEKCIiIpkV8SNKRkVEmrGMLfsC3AFEgJnlFwXPdc59zzm30MweBxYCCeB76gYV\nERHZPU1tyK6IiOxdMpaQOuf672Tfr4Bf7cFwREREREREZA/TGBgREZFmrCn1kKbSKVYVrcp0GCIi\nsgcpIRUREZFGYVXRKg6999BKZTe+eiO/nP3LDEUkIiINLZPXkIqIiEgDa0o9pJtjm8mN5lYq+9mo\nn2lSI5FmolevXlhT+lBqZHr16pXpEBqEElIRERFpFOKpOH3a9qlUFvbDGYpGROrbF198kekQpBHS\nT44iIiLSKAztMpTnzn4u02GIiMgepIRURESkGdPoOBERacyUkIqIiIiIiEhGKCEVERFpxpp6D+mj\nHz3KxGcmZjoMERFpIEpIRUREpFEoLC1kc2xzpbKQF6IsWZahiEREpKEpIRUREZFG4e55dzNtwbRK\nZRE/QjwVz1BEIiLS0Mw5l+kYdpuZuaYcv4iISEMygz/8AS6/PNOR7L5UOoXDEfK0Up2ISHXMDOdc\nk71AQ5/uIiIi0mj5np/pEEREpAFpyK6IiEgz1tQnNRIRkeZNCamIiIiIiIhkhBJSERGRZsxvQiNe\nl29aTiKVyHQYIiKyBykhFRERacZCTWi2iLEPjOXLzV9WKlv81WIO+MsBGYpIREQamhJSERGRZqwp\nJaRF8SJyo7mVyvZtty/zL5mfoYhERKShNaGvKREREfm6mtKQ3fY57WkVbVWpzDMPz9fv5yIizZUS\nUhERkWasKfWQfvKDTzIdgoiI7GH6yVFERKQZa0oJqYiI7H2UkIqIiDRjSkhFRKQxU0IqIiLSjDX1\nhDSZTpJ1Y1amwxARkQaihFRERKQZayqTGn26/lOK48VVyn3ziafipNKpDEQlIiINTQmpiIhIM5bV\nRDoXb3vjNj4u+LhKuZkR8SMk0okMRCUiIg3NnHOZjmG3mZlryvGLiIg0lGQSwmF49VUYMybT0dRN\nIpUg5IUws0yHIiLS6JgZzrkm+wHZxK8sERERkeoUl49+bQ45XNgPZzoEERFpIBqyKyIi0gwVV70c\nU0REpNFRQioiItIMbdmS6QhERER2TQmpiIhIM1RUlOkIam/O8jlsKtuU6TBERCQDlJCKiIg0Q+vW\nZTqC2pv0j0ms2bKmxv1D7x7Kh2s/3IMRiYjInqJJjURERJqhtWszHUHtbI5tZs2WNfTL61djnbe/\n/TYhT/9lERFpjvTpLiIi0gw1lYR0Y9lGLhp6Eb7n11hHs+yKiDRfGrIrIiLSDK1cmekIaqdn6578\n8YQ/ZjoMERHJECWkIiIizdAnn2Q6AhERkV1TQioiItIMLV4MnTtnOgoREckEM7vPzNaa2YLtyh41\ns/nl21Izm19D2x+b2UdmtsDMpplZpCFjVUIqIiLSzGzYAIWF0Lt3piOpH2c8cQZPL3o602GIiDQl\nDwDHb1/gnJvgnBvmnBsGPAVU+WA1s67AZcAw59yBBHMOTWjIQJWQioiINDNvvgkjRoBf8zxBjcK6\n4nU8/vHju6znm088Fd8DEYmINA/OudeBDTupciYwvYZ9PtDCzEJADrCqnsOrRAmpiIhIMzN7Nowe\nnekodq0oVsSyjct2WS/iR5SQiojUEzMbA6xxzn2+4z7n3Crgt8ByYCWw0Tn3ckPGo2VfREREmhHn\n4Kmn4PHH4b//zXQ0O9c3ry9Xjbpql/XuP+V+fGvk3b0iInvIrFmzmDVrVl0OcTY19I6aWRvgFKAX\nsAl40szOcc49Upcn3BklpCIiIs3Ie+8FSenQoZmOpP6EPP13RURkq/z8fPLz8yseT506tdZtzcwH\nvgUMq6HKMcD/nHOF5fWfBkYCDZaQasiuiIhIM3LHHXDhhWCW6UhERCTDrHzb3rHAovKhudVZDhxm\nZllmZsDRwKIGjFEJqYiISHOxdCnMmAGXXlrHAxUUBAcTEZEmycweAd4A9jWz5WZ2Qfmus9hhuK6Z\ndTGz5wGcc28DTwLvAR8QJLT3NGiszrmGPH6DMjPXlOMXERGpT6eeCoccAj//efB49Gi45ZavOcHR\n5s1Bg2HD4G9/a4gwAVj81WLmfjmX8w86v8GeQ0Rkb2BmOOea7LgY9ZCKiIg0Aw89BIsXw5VX1vFA\nS5dCixawaVO9xFWdguICTn30VIrjxbWqf/NrN/OL//6iweIREZHMUQ+piIhIE/fee3DccfDKKzB4\n8Lby3eohBXj5ZfjVr+A//6nXOLe69PlLaZ/TnhuOuqFW9VPpFGaGZ/odXURkR029h1TT1omIiDRh\nH38MJ50Ed91VORmtk9atg6G7DeQP4/5AxI/Uur7vackXEZHmSj81ioiINFGvvAJHHQW33QannVaP\nBx4wIJiut4FEQ1FM0wCLiAhKSEVERJqcZBJuugkmTIBHH4VzztnNA23aBE88UbW8VSs47LA6xVjp\nacoa7npUERFp2pSQioiINCHz5gXXhL7yCrz7Lhx55G4e6PnngzG+s2dDA87HsLFsI4ffdzixZKzB\nnkNERJouJaQiIiJNwJIlcP75MH48XHIJvPQSdO++GwdasADOOgsuvzyYmvdPf4IGHD7bJqsN733n\nPaKh6G4f4/GPH+esJ8+qx6hERKSxUEIqIiLSSDkHb78NEyfC4YdD796waBFceCF4u/MNfuedMG4c\nDB0aJKa73b1avS3xLSwqWFSlvC7JKEDICxFPxet0DBERaZyUkIqIiDQyGzbAPffAwQfD2WfDAQcE\nPaRTpkCbNnU48DnnBOuMXn11sNZoPVlXvI4rXryCXn/oxYMfPFhvx90q4keUkIqINFNa9kVERKQR\nKCyEf/4THnsMXnsNjj02WAr02GO/Zm9oQUFwXejpp1fd93Wy2e98B664Avbbb5dVQ16I7FA28y6e\nxz5t9/kawdbOif1P5MT+J9b7cUVEJPPUQyoiIpIB6TS88w7ccAOMHBkMx33yyaAT88svg/vHH1+L\nZDSVgrfeCrpPDz0U+vWD6dOhpKRuAS5aBGvWVCn+4b9+yOZY5TVK87LzuOnomxokGQXwzMMz/ZdF\nRKQ5yngPqZn9BPg10N45V1he9kfgBKAYON85934GQxQREamzWCyYFfe11+D112HOHOjcGU44AaZO\nhTFjICtrNw6cnx+M8T3hBLjlFhg1CiKRusfbIkp6/Vqydygf03MMrgFn5RURkb1LRhNSM+sOHAMs\n267sBKCvc66/mR0K3AXU32JoIiIiDSyRCDoY330X5s8PbhcsCEa/jhkDkycH14h26bKLA8Xj8Mkn\nwQFGjIBBg6rWmTlzNzPZ6t36+q38df5f+dXq5QxcNpxBnFlp/xmDzqi35xIREcl0D+nvgauAZ7cr\nOwV4CMA595aZtTazTs65tZkIUEREpCbOwYoVsHDhtu3DD+Gjj6BnTxg2LJiY6PTTg4ltc3NrcdDn\nn4dp04KDLFmy7UADBlRffzeT0Sc+foLcaC7H9zu+UvlxfY9j/H7jGbDwj3jRHrt1bBERkdrKWEJq\nZuOBFc65D63y+mfdgBXbPV5ZXqaEVEREMmLzZvj882BbsiTo/dy65ebCwIHBNmJEsFbokCHQqlU1\nB9q4MTjAZ59Bjx4wenTVOq1aBUuzXHUV7L8/ZO84aLZ2lhQuYU23V/jLkiWsbju8Ss9mj9Y9yAnn\nVGk3tMvQ4E7r1sELbwQ+Xf8pJ08/mcU/WJzpUEREpJ41aEJqZjOBTtsXAQ74P+DnwLHVNaumTBer\niIhIg4nHYeVKWL4cvvhiW/K5dSspgT59oG/fYBszJpiEduDAWkxc++9/BxeJLlkCZWXBpEP9+8NZ\nZ1Vf/4gjgq2W3l/zPss3Lefk/U6uVL6oYBFFrd+kRagfPVv3rNLusO67uBrmO98B3691HA2pf15/\nFn5/YabDEBGRBtCgCalzrrqEEzMbDPQGPrCge7Q7MN/MDgG+BLYfI9QdWFXTc0yZMqXifn5+Pvn5\n+XUNW0REmpFEAtauDWauXbEi2JYv33Z/xQpYvz64nrNHj2C22759gxlutyahnTtDxWCeNWuCZVXe\nWgXPrAoOtmxZ0D16xx1VAxg0CH7zmyAR7dRpuwPVzoK1C/jLO39h+eblDOk0hJuPvrnS/rRLk0wn\nq7Qbv994+i8cz6RJcGj3r/WUgT59dqNRwzAzrNrfq0VEpKmzxjBTnpktBYY55zaY2YnA951zJ5nZ\nYcAfnHPV/oxrZq4xxC8iInuWc1BUBKtXB/nhzm43boT27aF79yDh7NkzuK3YOpTRJfYFfmFBkLmu\nWhVsnTvDj35U9cnnzoXf/Q66dg2y2J49oVevIHPt1Klq/XIliRKWFC5hXfE6Ql6I/N75lfbP/Hwm\nd797N0+e+WSl8k/Xf8pLn79Ez9Y92b/9/vRv17/W79Po0cHEu9WNDBYRkebBzHDONdlf7TI9qdFW\njvKhus65F8zsRDNbQrDsywUZjUxERBpcOg2FhfDVV1BQsOvbgoJgfc4uXYK8cfvbgX1j9OF/dPYL\n6EABuWXr8NYXQNu2cNllVZ/8jfnBhZ8dOgQJZdeuwda/hsTvsMMoe+QhCooLiKfi9M3rW2n3ooJF\nTP9oOr888peVyhesXcDFz11Mh5wO5PfOr5KQju01llE9R1V5un3b7cu+7fb9Om+niIhIk9EoElLn\nXJ8dHv8gU7GIiMjui8eDJTG33woLay5bvz5ILjdsCObQad8+yAu3v+3ToYhveK/RrlMhbSkkN7mB\nFvFCIh3bwnaXbVR4f1FwfWbHjsFBtm7t2lWq5pxjc2wzGwZ2JfHmP6v0PC7ftJzpr9/Kz0b/rFL5\nvFXzGHnfSNrntOeI3kcw/bTplfZ3aNGBI3pVvQb0sO6H8eGlH9b43kVD0V28u3s35xz2NYc7i4hI\n49coElIREck856C4GDZtCiZX3fF2+/sbN1afaMZi0Ka1o0fbLXTKLYWOHWnbNuiczMsLhsiO6F3A\nwXP+SIuOm2iZt4nsXpsIl27C69AenniiamBLv4Lv/2nbQdq2hbx9gos9gVgyxqfrP2VTbBPOOcYc\nNCZYu7Pcl5u/5I9v/ZFfH3tOpcN+XPAxI+8bSdvstgzvOpynznyq0v6ccA692vSqEs6wLsOI/V+s\nxuSofU57ju5z9Nd892Vn0i5N+IYwyV8klZSKiDQzSkhFRJow56C0FLZsCa6p3LKl8lZUtC2ZrC7J\n3P62qChY0rJ9bpxh0Y/pkL2FDllF5EW2kBcuomNOiA2HTaZbt2B22a25YcfYCvr/+CRCtgnb7PKk\nEgAAIABJREFUvAnbUARl2dD+APjXm1ViThU4SgvitOzQN+gWbd0acnPZ0rYFj7x7D5tjmwl7YS4/\n7PKgwT77wAsvsHzTci557hL+fd6/Kx1v9ZbVnPP0OeRGc9mv3X6M6TWm0v7caC6je1a9iHJwx8Fs\nvqbmZU3a57RnwuAJVco982rzp2n6Vq+GH/6w+h8J9jDPPAwjmU4S9sOZDkdEROqRElIRkT0kHg+W\nD9m67Zg87phI1lReXJQmVLQBt6UYSkpoEy6mXXYJLbLTLOxwBC1bUmnrHCnkzHk/pYUrIie9hazk\nFrISRdCyFV/8ffbWfJDcXAiFgNXr4cQLcS1bksiOkmiRRSoni9y+A2G7yyKLYkU89vFjHDloIjz6\ncEViWeDHOO+58ymKFdHq78fz4nkvVnofVkfjnNLzZd695N1K5bGS9cz7z/3kRnPp1qpblfevc8vO\n3HFC1Vlse7fpvdOhsLnR3CpLokgthEIwa1amo6gQ8SPEU3ElpCIizYwSUhHZ6zkXLA1SUhIMWd0+\nadzxcW3KSovTZG1aiysphZISvNJivLISfFLMbXkMOTmQk7MtYeyQVcR3vriaTlZMC0rIccVkuRKI\nZvHylf+qqNeqVXmbkjX0Gz8Ya9MC65aDtWgBOTmkunZh9q0pjtrnqEqvr2hDgpk3rKIo4kjlZHPh\nET8NDtS6NUP6wVclX3HqY9/i1QteDRp06QLvvce6LWsZeOdAskPZ9G7Tm9cvrDz7a9qlWbZxGUSj\nMGRIRXmrZBlXHn4lLSMtaR1tXeX97p7bvUoyCtAupx33jL+nxr9TxI98rRlmpY5yc4Ouc+e+9lI1\nDaHomiJ8r3GsiyoiIvWnUSz7sru07ItI8+MclJUFW2npttua7tdYVuKIFycoTkSq1EuWJhiyYRZe\nrLRiy7YyohHHo20vrUgYy/M82kWK+MmHk8imlCxXSjRdSjRdRjoS5fGfzSGcXUY4K0bn3A7k5EBu\nspCDzh2Ii2ZRGoUWbTvitczB69wJHnuM0kQpv33zt5Qly/DM45eH/xzuvbfiCUsixq/evZ0bTr0d\nhg+veG/Wl6yn2++6EU/F6ZbbjRU/XlHpvdsc28xFz17EE2dUHmJZkijh92/+npxwDnnZeUw+aHKl\n/cl0kiWFSxjQfkDD/WElI+q87EtWVnBxcHZ2vcYlIiL1R8u+iEizkUoFk9Js3crKan5c231lZRAv\nS5P31aekS2O40jLSpcHOZCzFTH9cpUQyFoOW4RjX2s3khGK09MvI9mJk+zE837j9gPvIzg7+n7z1\ntrW/hZ89NpRwspRQcgvhVIxQIkYipy3PPrCe7Oygrh+J8/qGJzit66n0vvxWrEU2XotsyA4zb+MC\nirJ9Si74gLu+cVel92VLcZIbr1rILeP/wPZPXhRKc9XTLckqyqJji44sOXdJeYs8+GoNxfFiLnhm\nIk+f9XSl45kZpYlSskJZtM5qHRzvB9smF4+kkxwztBP0Hl6pXV52Hhuv3kjUj1Y7sUtuNLdKMgrB\n5DzXjr22xr97yAspGZXq5eYGFxkrIRURkQaiHlKRDHAOksngmsKtWyxW+fGuymIxiJU5ymJWJRGM\nl6Vpv24hqdI4riyGi8VxsTjpeJJZ2SdUSSaTpQkuLfsdoXScFn6MFn4ZOX4ZYd9xY5c/E41CJJoi\nkhMnkhWnjUX5/VuHE3ExIukywukYfroYz4tw6xXryMoKRnGGIgnmpx8knC7ixltvISenM0SiQQKW\nFSXdqiWXXdSVW8f+uSJpjEYhXraZaRMGUeqniYc9rsi/JtiRkwNnn01xvJjOv+1M0TVFwRuaTsOS\nJZSG4KjHTsRlZ5HVqi2zLnqt0vseS8a47F+XVRkWmkgl+PM7fyYrlEWLcAsmDplYaX/apVlYsJDB\nHQc36L8LkfpW5x7Sfv3ghRdgX62DKiLSWDX1HlIlpNKsOBf08iUS27avk+xVlMccybIkydIEqbIE\nydIE6dIYqbIEa7N7V2mfiKU5aNULuHgcLx7DEnEsEYdkkr9lX1rlOVKxJH+yy8jyYmSFSol6MbK8\nUsIuzGW9niUSgUgEwpE0sfZv06V0GPe/1o9wOk7IxQml44TSZaTNGP/TP3No6BKi5XleNAphr5jj\nb+xNblbPrQeCaASXncVp5/qkLY55jmkn/Cto4yfJuelaXMTjTwv+yo/yr96WAF50EWXJMnJuyiHi\nR2gVbUXBlWvh/fcrnjAe9jjtH+fw3AUvBRc6lkukEnz3+e8SDUXJCedw23G3Vfp7JdNJHnz/QS4a\ndlGl8rRL8+aKN4mGomSFsqokgs450i6t68lEdqHOCen8+TBgQPBZ0AhoLVIRkaqUkGaQEtL6l05X\nTua2JnTV3d/p47gjVVxKuixI6NKx8tt4knUt+1RtG3cMWPZi1QMlkzzX/oIqz5OIpbli/bV4qTge\npYQSDi+dwNIpLvYfIBR2+J0XEookCEeT5G4aypPrxhAhQdgShEng+RsIp3K45MglQQ9geQL4Wd7t\nhLwyZv3h/3B+hJQXJu2HSYWipCLZnHzV8Uxqd2dF0hiJgO/Fyfu/7sR9SIRgRJcTsGgELzvKxhv/\nhIUSjHysD4suXhG0CaXx/no3qZDPJS/+gFQkhEWjPHDGNDjxxIq/RzKdJP9v+bx+wWuwYkXFE6ZC\nPhf9+1L8SBbRcBZ3nnRnpb9jKp3i9rdu54rDr6j893Vpnv3kWSJ+hIgf4Zg+x1Ta75xj2aZl9G7T\nu0q5/hMo0vTUOSFtRIbePZQHTnmAgzoflOlQREQaFSWkGZTphDSdDoZdludNlW53vF/tvrgjGUuR\ncKGqdeKOFms+Jx1Pko4FiZxLJEnHk3ze8fBqj3XI/x6tKLBk+Y5kkukdL6+S0MViaa7dfCmWTuC7\nOF5ZNn46QcgluCD0d0IR8HvNJfurwwmHIRxyPLf2kCCRC39F2CUJuRTRVBbnjl5OJGqEwxAKOz7p\nMpWDN13PPdNytiVzXpiSUBEJ3+Oky0/kvOzHCIfLk8Aw+H6KfW9tT99WoyBUviMShkiYIWNfJEWS\ntEvx+snrg3ZhR97dv8KiIW569xdMPfp3+FkR/Kww3oXnk3ZpBt05iLAXJuJHeOfbb2Nvvhk8WThM\nOhzisplX8OdT7wnWOCznnONH//4RYT9od/PRN1f6mzvnuGveXVw64tIq5f9e8u+KRG/HdRCdc6ze\nspqurbo23D9IEZEdNKeENO3Se88asCIiX4MS0gwyM/fXH7zPynYHkkxZlaRw0JIZkEzi4pWTtH91\n+zaJpG3N1yh1G3Flrblk+XVYKoGfSmCpJITW4rkkP+p8DawdRipplY7/QvbBRErbELEEYUsSIUHI\nkhwx5rtYJI0fStLufz8gEg6Stf+8nxckdJQSToPvUqTM5+xvxfHDHm93PR/8BHhJxhVNZ8q0fXF+\nCOeFSPsh1vmf094fzKOXzSEc9QiFIBRy/KKgP2mS3PXYOoa3PQ0LhSASxsIhLByi5wF3MPekFNGI\nV5EEhkKO+3+Qiwv5pH2f6/Jvxs+O4EfD2Hnn4oBR949izoVztvWMvf02LhTiqlk/x8IRLBLh1hN+\nh/XoUbEkgHOOKbOmMCV/SpUetfvm30fYDxP2wkwYPKHK/jnL5zCyx8gq5Ss2rahol5edp546EZFa\nak4JqYiIVE8JaQaZmVvV8UDuung+oajPne5AfpT1HtGITygEJ937TfA95sefJ+6lSXiO0e3O5+Mf\n3E0o6gc9eiE4eU4e/z66gF4P3YIX9vEiISwS5i+f3EIq5POPkR2YOWFexXG3tpt67aFcf/QN+JHo\ntsJwmEtW3InvhQh5IW4/4fZtv+gWFkI4zG/e+j1Xjv0ZXjhSaW23B99/kFB5uzMHnVkl8Xp12auM\n6TmmSvln6z8j7IcJeSG6tepWZX8qndK1diIieyElpCIizZ8S0gzaccjuR+s+YlCHQVUSsvUl6ysS\ntuxQtnrYRERkr6CEVESk+WvqCWmzWoe0piUZ2uW028ORiIiINAOPPAIFBXD55ZmOhK0/QOtHZRGR\n5kWzA4iIiEj1Nm2CRYsyHQUAZz15Fk8ufDLTYYiISD1TQioiIiLVa906SEobgbAfJp6KZzoMERGp\nZ0pIRUREpHq5ubB5c6ajACDiR5SQiog0Q83qGlIRERGpR42oh/S+k+/TOqQiIs2QPtlFRESkeq1b\nN5oeUiWjIiLNkz7dRUREpHr9+8Pf/57pKEREpBlTQioiIiLVy86GAw/MdBQiItKMKSEVERGRJmHr\nWqQiItJ8KCEVERGRRu/m127m2v9em+kwRESknmmWXREREWn0rhl9DWaW6TBERKSeqYdUREREGj0l\noyIizZMSUhEREanZBRfABx9kOgoREWmmlJCKiIhIzb78EtauzXQUIiLSTCkhFRERkZrl5sLmzZmO\nAtAsuyIizZESUhEREalZI0lIn/j4Cc588sxMhyEiIvVMCamIiIjUrHVr2LQp01EQ8SPEU/FMhyEi\nIvVMCamIiIjUrJH0kCohFRFpnrQOqYiIiNTs0kszHQEA4/qNY1y/cZkOQ0RE6pkSUhEREalZly6Z\njgDQOqQiIs2VhuyKiIiIiIhIRighFRERERERkYxQQioiIiJNQtqlMx2CiIjUMyWkIiIi0ugtKVzC\nvnfsm+kwRESknikhFRERkZqtWwfHHJPpKOjbti+fXfZZpsMQEZF6poRUREREapaVBW+/nekoMDPN\ntCsiUktmdp+ZrTWzBduVPWpm88u3pWY2v4a2rc3sCTNbZGYfm9mhDRmrln0RERGRmrVsCcXFkEqB\n72c6GhERqZ0HgDuAh7YWOOcmbL1vZrcBG2toezvwgnPuDDMLATkNGah6SEVERKRmnhckpVu2ZDoS\nERGpJefc68CGnVQ5E5i+Y6GZtQLGOOceKD9O0jm3uWGiDCghFRERkZ3LzYVNmzIdhWbZFRGpB2Y2\nBljjnPu8mt19gK/M7IHyob33mFl2Q8ajIbsiIiKyc7m5sLlBfyDfJecc/i99Utel8Ey/p4vI3mvW\nrFnMmjWrLoc4m2p6R8uFgGHA951z88zsD8DVwPV1ecKdUUIqIiIiOzdjBnTvntEQzIyIHyGRShAN\nRTMai4hIJuXn55Ofn1/xeOrUqbVua2Y+8C2CpLM6XwIrnHPzyh8/CfxstwKtJf3EKCIiIjvXr18w\n226GRf0osVQs02GIiDQVVr5t71hgkXNuVXUNnHNrgRVmtnXh56OBhQ0XohJSERERaSI2Xb2J3Ghu\npsMQEWn0zOwR4A1gXzNbbmYXlO86ix2G65pZFzN7fruiHwLTzOx9YAhwc0PGqiG7IiIi0iRoHVIR\nkdpxzp1TQ/kF1ZStBr6x3eMPgBENF11l6iEVERERERGRjFBCKiIiIiIiIhmhIbsiIiKyc9Omwf/+\nB7/4RUbDSLs0hmnorkgzkU5DKlXzbW3L0ilHKpEmnUyTTqSC22SaRLRl1XaJNNF1K0gnUrhUGpcs\nr5+GDZ33r1I/HU/S6fM3cMmgwCWDdinnsaTPcZViSaRSkCjlgIWP46XA0kAqhUunSeEzc79v4SVb\nQCpa0c5LxDju01/gXAmkU0QSuXgpnyRhHuwzlXQaNmV9RKS0OxZrQyhRyo+WXo65FKnQGvBKM/1n\nrDMlpCIiIrJzsViQkGbYiL+O4J5v3MPBXQ/OdCjSRDm3e0nQThOkpIOyMlLxIFHZPiEqa9Wh6rGS\naXK+/LQisalIcFJQ0GNYledxiSQ9Pv0PpFKkU2lIlic4aY+F/U8hlYJ4KkEqnSSRShFK+By28GGs\nPBGyVIqE24xzOczsf0WlYxd5y3Dprzhv4V1EEq3wkyFwaSydIkGY3ww6l1BxT7x424q4Qskyfr7u\nVMxK8FySUFk7/GSYGBG+n/coqRSUdv8Xtu5ArKgbkWQJT24Zh0cKL6sA88rwSVFW2oXRqXn4Pvg+\neB6kD7qP1iuG8+6qk/EthUeaECnCfjElYePAU8+izaIfk71lEJ4XtGvpinhrUS5pjLT5pPFIm0+J\n34oh553Pltx5YCn2W3YbeWXDyUmXcOerY3HmURouJJRujVkLYqGWTDnpHXwfXuswkbXRV3GW4qT1\nD3DTP24A83Cej/M81oQ+IDu0H4t6HIfvQzgcxPK31Amsiv+Xe1Y5+oaPppXfHXwPPJ90NJvb+77I\nMa0uY/+csRWvOZyG2EMvUphaifM8hrc9lQ5ZvXBZWbQ/PTjuX5bey3Gdz2VgmxGE0j6d/jUc8z1e\nKfgHa+Ib4MFMn1l1Y865TMew28zMNeX4RUREGtLo0XDLLcFtnTz5JDzyCDz9dL3Etbucc02yd9S5\n3Ut0ar0v5bCNG7b19JT3/KSSjpJ2ParUd8kUuV8sCBKcZLriNp2Glb1HVXmedDxJv8XPBz09qXTF\nbRqPt/p/k0QqQcqlsGQOlg5DIsHYj/+CpVMk3Sa8VBg/5ZHE55l+PyWVgi2hpcRsEymXolVRDy5e\nfCuWTmHpFLg0KX8tLtWGX3e9t1LsW/LmYP4y/rTwdkKlefjJMObSeC5IoM444Dt46w7Ebdr2uiPJ\nEmaGDsD3yvBJ45Xl4qd84hZlZNZ7+D4kh/yVyKqxRIr2o6UV8876ffBdCvNL8Uji4YjRitH9CisS\nCd+HtQOm0nXNscyZfTRpCxIhh0ciFGNLxHHgxSMYuOK35JUeUtEu25Xwy9ldCaXb4llLnHk484mF\nW3DS5J6sjs4ibSlO2PIoPdP5RNOlXPDsqeB5rPLn05o+ZPudSYWzeXbCI3ge/C1xIp+n/4uHz6XZ\nT3LxjGcqEiF8jw/jL9GlxXC+nDitInbPg7tWfZsvit7g0lcLOTTvVDrm7AMhH8/3cJEovxjyP47t\nPIHBeSMq2vnpBPMfOIPVsRXg+xzb+wx65PbBohHix4/H8+CvH/2Wo3qNY1CHQXguReSdOXghj+eW\nPsvKkpV4IZ9TBp9O7/yTK50rj370KId2GcE+W0JsH+i/l85kdfFakm1bc2L/E+mW263SCfbqslfZ\nr/0AOrXsVOl47656lw1lG/DNZ0jnIeRl51Xav6RwCZ1adKJVtFWl8tVFq4mlYvjm06FFB7JClZe9\nKkmUEPWj+J5fD58QdWdmOOea3odjOSWkIiIizdTo0fDLX8Jhh9UtCWrxxkw6P/ArFv32BZKhrKr1\nk45wwaqKhKgiMUo5NnXat+oQuESK9p+/VdEztDUhcg7+1+eYKvVdIsmghU9UDH0jmSKZLiPlUsze\n7xz8VEtIRSrakUhw9OIphJIR/LRX0UuUxuOugecR9zaSSqfJKt4XL94GLxHjsv/9GEunSIXX4idy\nCCXDJFyI/+tyH+k0bGn3KomsVaRcitwvD+evyy/DXCpIWlway1pHIt6Gk8OvVX7/9n+UrNaLeOf9\nv+CXtSKU9vFI45MiYREOGnUl0TVjiWweUJEktLAS5q3KCxIh5/Cdh+fSxC3KqINK8DxYPeB62hWc\nQm7xMHIo4cm3uuPMIx4qJeUlSJmRtk788Ojl2xIJH97pcjEDN07gjhk/CRIhz8eZx4bw52yObOak\nibmcWPIY+3BURbuIizHqmV60tX7k+F2CID2fVCSLCacUsCT5XzzzuTj3GQZlH03YxTn0yasw3+P9\n2Iv0yBpCu2hvXCTKJ2dPCRKhld/hk+K5+Obzw563c8Irb2N+8IQW8nl1/Qz2bXcI0XNurNSDdtfi\nqSwpfJ8xH6xlXK/T6dG2L17Yx3wPLxrmzryFHNnrGAZ2GLStnUvxwpPXsWLLSrxQiJMHnkqvvH0g\nFIL99gPgiY+fYHjX4ezTdp/gF4SCAvA8XvriP6wuXosXCnN032Pp2rlfpXNszvI59MvrVyURen/N\n+2ws24hvPoM7DqZtdttK+5duWEr7nPZVEqF1xeuIp+L45pOXnUc0FK20P5aMEfJCjSYRksZBCWkG\nKSEVERGp2be+Bf/8J5V6dLZPTnYsq2lf3/gipn1yMAmLMO7QjVXqZ1HGA6/1xW3tISrv8YmHsrnm\nxAVV6kfSZVz5wlHgBfWcFyRGqVAWD57xfJUYQuk4pzw9Oajv+5jnsTj9CgXean78jRZ8p+0TDM45\nplIPDg8Po0f2EDpk7RP0FPk+hCNcddgKPimei4fHD/r9kQPbHo6XTtLtn/eA5zH7qxnslzec7rl9\nIRKhaPw5+D7cvegmPt20AN98vjf4Jwz/eDWe7wXJUMjnhWX/YEi3Q+h54sRKsf9twf18sX4JnVcU\nMn7AKfRq12fbiwuFeLp4HsO6DKN3m97b/nDOMfODZ1hTsg4LhTiq7zF0bdMjaFPeQzz3y7n0aduH\nji06VvqbL1i7gE1lm/A9n4EdBtImq02l/cs2LqNdTjtaRlpWKv+q5CsSqQS+59Mmqw0RP1Jp/9Z9\nnmk+TJHGRglpBikhFRERERGRvVlTT0gz+jOXmV1mZovN7EMzu2W78mvM7DMzW2Rmx2UyRhERERER\nEWkYGZtl18zygfHAYOdc0szal5fvD5wJ7A90B142s/7qChUREREREWleMtlDeilwi3MuCeCc+6q8\n/BTgUedc0jn3BfAZcEhmQhQREREREZGGksmEdF9grJnNNbNXzGzromLdgBXb1VtZXiYiIiIiIiLN\nSIMO2TWzmcD282Ab4ID/K3/uNs65w8xsBPAE0Ke8zo40XFdERERERKSZadCE1Dl3bE37zOy7wNPl\n9d4xs5SZtQO+BHpuV7U7sKqm40yZMqXifn5+Pvn5+XULWkRERERERPaIjC37YmaXAN2cc9eb2b7A\nTOdcLzMbCEwDDiUYqjsTqHZSIy37IiIiIiIie7OmvuxLxmbZBR4A7jezD4EYMAnAObfQzB4HFgIJ\n4HvKOkVERERERJqfjPWQ1gf1kIqIiIiIyN6sqfeQZnKWXREREREREdmLKSEVERERERGRjFBCKiIi\nIiIiIhmhhFREREREREQyQgmpiIiIiIiIZIQSUhEREREREckIJaQiIiIiIiKSEUpIRUREREREJCOU\nkIrsxWbNmpXpEESaPJ1HInWjc0hk76aEVGQvpv8EiNSdziORutE5JLJ3U0IqIiIiIiIiGaGEVERE\nRERERDLCnHOZjmG3mVnTDV5ERERERKQeOOcs0zHsriadkIqIiIiIiEjTpSG7IiIiIiIikhFKSEVE\nRERERCQjGm1CamaXm9mH5dsPy8vamtlLZvaJmb1oZq1raDvZzD4trzdpz0Yu0jjU8RxKmdl8M3vP\nzP6xZyMXaRxqOIdON7OPys+RYTtpO87MFpd/F/1sz0Ut0njU8Rz6wsw+KP8eenvPRS3SuNRwHv3a\nzBaZ2ftm9pSZ5dbQtkl8FzXKa0jNbBAwHRgBJIF/Ad8DLgbWO+d+Xf6mtnXOXb1D27bAPGAYYMC7\nwDDn3KY9+BJEMqou51B5+83OuWo/3ET2BjWcQ5cCISAN3A38xDk3v5q2HvApcDSwCngHmOCcW7xn\nohfJvLqcQ+Xt/wcc7JzbsGciFml8dnIe7QP81zmXNrNbAOecu2aHtk3mu6ix9pDuD8x1zsWccyng\nVeCbwMnAg+V1HgROrabt8cBLzrlNzrmNwEvAuD0Qs0hjUpdzCIIfc0T2ZtWeQ865T5xzn7Hzc+QQ\n4DPn3DLnXAJ4FDil4UMWaVTqcg5Rvr+x/j9VZE+p6Tx62TmXLq8zF+heTdsm813UWE/0j4Cx5cML\nc4ATgR5AJ+fcWgDn3BqgQzVtuwErtnu8srxMZG9Sl3MIIGpmb5vZG2bWKD+8RBpYTedQbez4PfQl\n+h6SvU9dziEAB7xoZu+Y2cUNEqFI41eb8+hCgp7THTWZ76JQpgOojnNusZndCrwMFAHvE3RT10Z1\nv7g1vnHJIg2ojucQQE/n3Boz2wf4r5ktcM4tbYhYRRojfQ+J1E09fA+NLP8e6gDMNLNFzrnXGyJW\nkcZqV+eRmV0LJJxzj1TTvMl8FzXWHlKccw845w52zuUDGwjGQK81s04AZtYZWFdN0y+Bnts97k4w\nblpkr1KHc2hr7ynlSegsYOieiFmkManmHPqslk31PSRCnc6h7b+HCoBnCIYfiux1ajqPzGwyQY/p\nOTU0bTLfRY02IS3/RQwz60lw7dt04Fng/PIqk4EZ1TR9ETjWzFqXT3B0bHmZyF5ld88hM2tjZpHy\n++2BkcDCPRCySKNSwzlUqUoNTd8B+plZr/JzaQLBuSeyV9ndc8jMcsysZfn9FsBxBEMXRfY61Z1H\nZjYO+ClwsnMuVkPTJvNd1Chn2QUws1eBPCAB/Ng5N8vM8oDHCcZOLwfOcM5tNLODge845y4pb3s+\ncC1Bt/SNzrmHMvEaRDJpd88hMzucYPbDFMGPVr93zv0tIy9CJINqOIdOBe4A2gMbgfedcyeYWRfg\nr879f3t372JHFcZx/PvTRdSE4FuTToUoIsZFFhVfUNBC0SKCjYLYKcSXmD9ALRIwWgTSCRpJoYUi\n2MSowaCNYBDBuBEVUfsVqxjWF7KPxU509k3Z3Xs5N3u/n2Zmzj3nOWcuDHMfzpm59WDX9j7gAPPX\n0MGq2tfkJKSG1noNdY+LvMf877gJ4C2vIY2rFa6jH4ALgF+7ap9X1c5z9V40sgmpJEmSJGljG9kl\nu5IkSZKkjc2EVJIkSZLUhAmpJEmSJKkJE1JJkiRJUhMmpJIkSZKkJkxIJUmSJElNmJBKkiRJkpqY\naD0ASZKGIcllwDGggK3AGWAGCHC6qu4YQp+TwM6qemKdcZ5ifoyHBjIwSZJGVKqq9RgkSRqqJC8A\nv1XV/iH38w6wp6qm1xnnIuCzqrppMCOTJGk0uWRXkjQOsuAgOdVt70ryaZK3k3yX5KUkjyY5nuRE\nkqu6elckebcrP57ktiUdJJuBG84mo0leTHIoyUdJfkryUJKXk3yd5EiS87t6+5J8k+SrJK8AVNUs\n8HOSqeF+LZIktWVCKkkaR/3lQduBZ7rtY8C2qroFONiVAxwA9nflDwOvLxNzCji5qOxXfPPKAAAB\ne0lEQVRq4H5gB/AmcKyqtgO/Aw8kuRTYUVXXV9UksLfX9kvgzrWfoiRJo89nSCVJ4+6LqpoBSPIj\ncLQrnwbu7vbvBa5LcnamdXOSTVV1uhdnK/DLotgfVNVckmngvKrqx74SeB+YTfIacAQ43Gs7A1y7\n3pOTJGmUmZBKksbdH739ud7xHP/eJwPcWlV//kecWeDC5WJXVSX5a1E/E1V1JsnNwD3AI8DT3T5d\nrNlVnoskSecUl+xKksZR/r/KAkeBZ/9pnNy4TJ1vgW2r6TPJxcAlVfUhsBvox72GpUuAJUnaUExI\nJUnjaKVXzK9UvguY6l50dBJ4cknDqu+BLUk2rSL2FuBwkhPAJ8Bzvc9uBz5eIZYkSRuCf/siSdKA\nJNkFnKqqN9YZZxLYXVWPD2ZkkiSNJmdIJUkanFdZ+EzqWl0OPD+AOJIkjTRnSCVJkiRJTThDKkmS\nJElqwoRUkiRJktSECakkSZIkqQkTUkmSJElSEyakkiRJkqQm/gaClhIefV2hCwAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feb77262cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "\n",
    "# Plot the potentials\n",
    "ax.plot(t_ref, y_ref[:,0], linestyle=\"-\", label=\"V ref.\")\n",
    "ax.plot(t_old, y_old[:,0], linestyle=\"-.\", label=\"V old\")\n",
    "ax.plot(t_new, y_new[:,0], linestyle=\"--\", label=\"V new\")\n",
    "\n",
    "# Plot the adaptation variables\n",
    "ax2.plot(t_ref, y_ref[:,1], linestyle=\"-\", c=\"k\", label=\"w ref.\")\n",
    "ax2.plot(t_old, y_old[:,1], linestyle=\"-.\", c=\"y\", label=\"w old\")\n",
    "ax2.plot(t_new, y_new[:,1], linestyle=\"--\", c=\"m\", label=\"w new\")\n",
    "\n",
    "ax.set_xlim([90., 92.])\n",
    "ax.set_ylim([-65., 40.])\n",
    "ax.set_xlabel(\"Time (ms)\")\n",
    "ax.set_ylabel(\"V (mV)\")\n",
    "ax2.set_ylim([17.5, 18.5])\n",
    "ax2.set_ylabel(\"w (pA)\")\n",
    "ax.legend(loc=5)\n",
    "ax2.legend(loc=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare properties at spike times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spike times:\n",
      "-----------\n",
      "ref [ 18.715  30.561  42.495  54.517  66.626  78.819  91.096]\n",
      "old [ 18.73  30.59  42.54  54.58  66.71  78.92  91.22]\n",
      "new [ 18.72  30.57  42.51  54.54  66.66  78.86  91.14]\n",
      "\n",
      "V at spike time:\n",
      "---------------\n",
      "ref [ 0.006  0.03   0.025  0.036  0.033  0.031  0.041]\n",
      "old [  6.128   5.615   6.107  10.186  17.895   4.997  20.766]\n",
      "new [ 32413643.009  32591616.327  35974587.741  51016349.639  77907589.627\n",
      "  37451353.637  11279320.151]\n",
      "\n",
      "w at spike time:\n",
      "---------------\n",
      "ref [  7.359   9.328  11.235  13.08   14.864  16.589  18.256]\n",
      "old [  7.367   9.344  11.258  13.111  14.906  16.637  18.315]\n",
      "new [  7.362   9.334  11.244  13.093  14.883  16.611  18.278]\n"
     ]
    }
   ],
   "source": [
    "print(\"spike times:\\n-----------\")\n",
    "print(\"ref\", np.around(s_ref, 3)) # ref lsodar\n",
    "print(\"old\", np.around(s_old, 3))\n",
    "print(\"new\", np.around(s_new, 3))\n",
    "\n",
    "print(\"\\nV at spike time:\\n---------------\")\n",
    "print(\"ref\", np.around(vs_ref, 3)) # ref lsodar\n",
    "print(\"old\", np.around(vs_old, 3))\n",
    "print(\"new\", np.around(vs_new, 3))\n",
    "\n",
    "print(\"\\nw at spike time:\\n---------------\")\n",
    "print(\"ref\", np.around(ws_ref, 3)) # ref lsodar\n",
    "print(\"old\", np.around(ws_old, 3))\n",
    "print(\"new\", np.around(ws_new, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Size of minimal integration timestep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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7LD29Gu8GHjzzpxdOrp64ORXPZa4OrqWeG29nb8J8wvQaUwmErEyka+GHhWgM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feb7be9bcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogy(t_ref, h_ref, label='Reference')\n",
    "plt.semilogy(t_old[1:], [d['hu'] for d in fo_old], linewidth=2, label='Old')\n",
    "plt.semilogy(t_new[1:], [d['hu'] for d in fo_new], label='New')\n",
    "\n",
    "plt.legend(loc=6)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convergence towards LSODAR reference with step size\n",
    "### Zoom out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IMV0I8a0QoloI0bfOfS8LIS4KIb4TQoyWlfFBs27dOg6D94DrEKrj++9DkZKy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\nADAoBSEAAMCgFIQAAACDUhACAAAM6guS3lxxgOmS3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feb7722dcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t_ref, y_ref[:,0], label=\"V ref.\")\n",
    "resolutions = (0.1, 0.01, 0.001)\n",
    "di_res = {}\n",
    "\n",
    "for resol in resolutions:\n",
    "    t_old, y_old, _, _, _, _ = scipy_aeif(p, rhs_aeif_old, simtime, resol)\n",
    "    t_new, y_new, _, _, _, _ = scipy_aeif(p, rhs_aeif_new, simtime, resol)\n",
    "    di_res[resol] = (t_old, y_old, t_new, y_new)\n",
    "    plt.plot(t_old, y_old[:,0], linestyle=\":\", label=\"V old, r={}\".format(resol))\n",
    "    plt.plot(t_new, y_new[:,0], linestyle=\"--\", linewidth=1.5, label=\"V new, r={}\".format(resol))\n",
    "plt.xlim(0., simtime)\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.ylabel(\"V (mV)\")\n",
    "plt.legend(loc=2)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zoom in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VVxEWFuZqS01NdRsrIiKdS/0ew+ZYlkWVZakwFBHxMn9fJ+AruSbX9XOGleH2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\narj/B8lKwsSGkSHrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feb76de6208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t_ref, y_ref[:,0], label=\"V ref.\")\n",
    "for resol in resolutions:\n",
    "    t_old, y_old = di_res[resol][:2]\n",
    "    t_new, y_new = di_res[resol][2:]\n",
    "    plt.plot(t_old, y_old[:,0], linestyle=\"--\", label=\"V old, r={}\".format(resol))\n",
    "    plt.plot(t_new, y_new[:,0], linestyle=\"-.\", linewidth=2., label=\"V new, r={}\".format(resol))\n",
    "plt.xlim(90., 92.)\n",
    "plt.ylim([-62., 2.])\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.ylabel(\"V (mV)\")\n",
    "plt.legend(loc=2)\n",
    "plt.show();"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
